automation and control engineering - ingegneria dell'automazione

The course presents advanced methods for the analysis of dynamical systems and for the synthesis of multi-input, multi-output controllers.
In the first part, the concepts and tools include Lyapunov stability theory for nonlinear systems, small gain theorem for nonlinear systems, structural properties of linear dynamical systems (poles, invariant zeros, principal gains), performance and robustness indices and basic robust control concepts, state feedback via pole placement, output feedback via state observers.
In the second part, particular emphasis is given to Model Predictive Control (MPC) techniques to deal with systems operating under state/input constraints and possibly with nonlinearities and integer decision variables, focusing on formulations that are well adopted in real-world industrial solutions.

Knowledge and understanding (Dublin Descriptor 1, DD1)
Upon successful completion of the course, students will be able to:
apply Lyapunov stability theory to the analysis of nonlinear systems
evaluate the structural properties of linear dynamical systems, as well as the performance and robustness of linear control systems
understand how the regulator structure affects both static and dynamic performance
design multivariable control systems using pole placement, LQ, LQG
design dynamic, possibly reduced-order, observers via pole placement and Kalman filtering
formulate model predictive control problems in state-space form and using input–output models
Applying knowledge and understanding (DD2)
Through the exercise and lab sessions, students will learn to:
perform stability analysis of dynamical systems and design nonlinear controllers using Lyapunov stability theory
analyze multivariable linear systems in terms of reachability, observability, gain, singular values, poles, and zeros, and understand the proper formulation of control design objectives
select an appropriate regulator structure to achieve the desired control objectives
apply advanced control design methods (pole placement, LQ, LQG, and model predictive control) to multivariable systems and tune the associated parameters
design dynamic observers based on pole placement and Kalman filtering for disturbance estimation and output-feedback control
use software tools to simulate and analyze dynamical systems, design controllers using the methods covered in the course, evaluate closed-loop performance, and assess the sensitivity of the results to design choices and tuning parameters
Making judgements (DD3)
Upon successful completion of the course, students will be able to
carry out a preliminary system analysis, identifying the control and controlled variables and selecting an appropriate control structure
assess the limits of achievable performance and define suitable control specifications
choose the most appropriate control design methodology
critically evaluate the obtained results and their sensitivity to the adopted design choices
Communication skills (DD4)
Through software labs and optional project work, students will learn to communicate clearly, concisely, and effectively the control synthesis problem addressed, the chosen solution method, and the results obtained.
Learning skills (DD5)
Through lab exercises and optional project work, students will learn to tackle real-world problems – both independently and as part of a team – using the advanced control methods covered in the course.

Nonlinear systems: equilibria, stability, Lyapunov theorems. Synthesis of the control law with control Lyapunov functions and the backstepping procedure. Norms and gain of dynamical systems. Small gain theorem.
Multivariable feedback control systems: Structural properties: reachability, observability, poles, and invariant zeros. Nominal and robust stability of multivariable feedback systems. Static and dynamic performance. Schemes with integral action. Pole placement control of single-input/single-output and multivariable systems.
State and disturbance observers: State observers and dynamic output feedback. Disturbance estimation with state observers.
Model Predictive Control (MPC) for linear systems: Unconstrained optimal control, linear quadratic control, optimal filtering and LQG control. Constrained optimal control via MPC; moving horizon estimation.
Industrial formulations of MPC: soft constraints, impulse and step response models, move blocking and input parametrization.
Advanced MPC schemes: MPC for systems with nonlinear dynamics and with integer decision variables.

Prova scritta obbligatoria, senza prove in itinere
Prova orale condizionata (a scelta del docente)
Progetto/i - Elaborato/i facoltativo (a scelta dello studente), di gruppo, senza revisioni periodiche, senza presentazione/discussione finale
The final assessment will be a written exam consisting of numerical exercises and theoretical questions.
During the exam the student must prove to be able to design a controller for multivariable systems, and in particular to:
perform a preliminary analysis, specifying the control and controlled variables and the adopted control structure (DD1, DD2)
evaluate the limits to the achievable performance, define the control specifications (DD3)
apply modern control design techniques (DD2)
critically evaluate the achieved results and their sensitivity to the adopted design choices (DD5)
describe the achieved results in a clear and convincing way (DD4)
At the instructor's discretion, students may be required to take an oral examination to confirm their knowledge, if deemed necessary

L. Magni, R. Scattolini, Advanced and Multivariable Control, Editore: Pitagora Editrice, Anno edizione: 2014
H. K. Khalil, Nonlinear Systems, Editore: Prentice Hall, Anno edizione: 1996
J.M. Maciejowski, Multivariable Feedback Design, Editore: Addison-Wesley, Anno edizione: 1989
S. Skogestad, I. Postlethwaite, Multivariable Feedback Control, Editore: Wiley, Anno edizione: 1996
T. Glad, L. Ljung, Control Theory, Editore: Taylor & Francis, Anno edizione: 2000

This course introduces the student to the fundamentals of manufacturing processes used in discrete-part fabrication, with particular attention to their automation, control, and integration within digital and smart manufacturing systems.

Lectures will allow students to:
·know the fundamentals of manufacturing processes used in discrete-part fabrication,
·understand the classification of manufacturing processes and systems,
·understand the role of manufacturing quality, inspection, and cost in process design and evaluation,
·know the basic principles of manufacturing automation systems, including Cartesian, antropomorphic, and scanning systems,
·understand the fundamentals of numerical control programming and machine control systems,
·understand the principles of statistical quality monitoring in discrete manufacturing processes,
·understand the smart manufacturing paradigm and the role of process data for in-line monitoring and inspection in modern production systems,
Students will carry out laboratory and exercise activities that will allow them to:
·develop the capability of taking autonomous decisions in an industrial-like activity, applying the theoretical and practical knowledge acquired during lectures to unstructured problems.
With respect to the study programme, the course will allow to reach the following generic learning outcomes:
·capability of knowledge and understanding (DdD1),
·capability of applying knowledge and understanding (DdD2),
·capability of making judgements (DdD3),
·capability of communication (DdD4).

Manufacturing theory: Introduction to manufacturing and to the role of production technologies in discrete-part fabrication. Engineering materials and their relevance for manufacturing process selection. Fundamentals of process physics and transformation theory. Overview and classification of the main manufacturing processes. Manufacturing quality, inspection, and cost.
Manufacturing automation: Introduction to automation systems for manufacturing. Cartesian systems and their applications in automated production environments. Robotic systems for manufacturing operations. Sensing architectures for inspection, measurement, and in-line digital data acquisition. Numerical control programming and basic programming logic for automated manufacturing equipment, with laboratory activities focused on the connection between programming, control, and execution.
Smart manufacturing: Introduction to the smart manufacturing paradigm, fundamentals of statistical quality monitoring, from product inspection to process monitoring, sensors for manufacturing process quality and their integration into production equipment, data mining and analysis methods as part of automation architectures supporting in-line process monitoring and decision-making in manufacturing.
Manufacturing automation lab: Hands-on activities covering the digital manufacturing workflow, from CAD to CAM programming and CNC execution. Students will integrate computer-aided design, NC coding, automation technologies and in-line sensor data analytics for quality prediction and manufacturing performance improvement.

Prova scritta obbligatoria, senza prove in itinere
Prova orale obbligatoria
Progetto/i - Elaborato/i facoltativo (a scelta dello studente), di gruppo, senza revisioni periodiche, senza presentazione/discussione finale
The final evaluation will consist of a written and oral exam. The written exam will consist of numerical questions and software exercises (DdD1, DdD2, DdD3). The students who get a sufficient mark from the written exam will proceed to the oral exam stage to discuss the topics of the course (DdD1, DdD2, DdD3. DdD4). The final mark will consist of the two marks and additional points taken from the lab work (DdD2, DdD4).

Mikell P. Groover, Automation, Production Systems, and Computer-Integrated Manufacturing, ISBN: 9780133499612
Mikell P. Groover, Fundamentals of Modern Manufacturing: Materials, Processes, and Systems, ISBN: 978-1-119-47521-7

The main objective of the course consists of the definition of the procedures for the design of the closed-loop controls of electromechanical devices.
To achieve this objective, different configurations of electrical machines, power supplies and control systems are presented, in order to highlight the main problems that a designer could meet in defining the control system of a particular electromechanical system.

Knowledge and understanding (DdD1)
After taking the exam the student:
knows the methodologies and the principles to describe the dynamic behavior of the main electric machines, starting from the electromagnetic structure of the machine
understands and knows the dynamic operating modes of the main electrical machines
knows how to identify the expressions and dependencies of the electromagnetic torque
knows the effect that the power supply has on the operating regions of the drive
Applying knowledge and understanding (DdD2)
During the laboratory activities, made using computer simulations tools, the student:
is able to analyse the behaviour of a dynamic system
is able to analyse the effect of the control loop on the performance of an electrical drive
After taking the exam the student:
is able to identify and calculate the main parameters of the electric drive control system
is able to define simulation environments for industrial processes whose automation is based on the presence of electric drives.
Communication (DdD4)
After taking the exam, the student is able to effectively communicate the results of the transient analysis of an electric machine and its control.

Lectures Program
The course develops the principles and control techniques of modern electrical drives, emphasizing their role in energy-efficient industrial automation, electric transportation, and sustainable electrification,
1. Principles of electrical engineering, electromechanical energy conversion and energy efficiency.
2. DC motor drives: steady-state and dynamic models; static power converters; flux, speed and torque control strategies; efficiency considerations and regenerative operation.
3. Induction motor drives: steady-state equivalent circuits; four- and five-parameter dynamic models; voltage-source and current-source inverters; state observers; field-oriented control (FOC); scalar control; Direct Self Control (DSC) and Direct Torque Control (DTC). Applications in high-efficiency industrial processes.
4. Permanent Magnet AC Brushless and Synchronous Reluctance drives: dynamic models; field-oriented control; comparison of machine technologies with respect to efficiency, rare-earth material usage and sustainability.
5. Brushless DC (BLDC) drive s: power converters, electronic commutation and control strategies. Applications in electric mobility and energy-efficient systems.
6. Switched Reluctance Machines (SRM): operating principles, power converters and control strategies. Sustainable machine design and rare-earth-free technologies.
Exercises and Computer Laboratory Activities
Students develop simulation and control algorithms for high-efficiency electric drives using numerical tools.
Design and implementation of the control of a DC motor drive.
Design and implementation of Field-Oriented Control (FOC) for an induction motor.
Design and implementation of scalar control for an induction motor.
Design and implementation of Direct Torque Control (DTC) for an induction motor.
Design and implementation of Field-Oriented Control (FOC) for a Permanent Magnet AC Brushless motor..

Prova orale obbligatoria
Progetto/i - Elaborato/i obbligatorio, individuale, senza revisioni periodiche, con presentazione/discussione finale
The exam consists of an oral test to verify the knowledge of the control of the electric drives and a discussion on a report of one of the excercise activities, taken during the semester.
The exam consists of two main questions on two different electrical drives in order to test the knowledge of the student about (DdD1, DdD2, DdD4):

• dynamic models of the electrical machine, starting from its structure
• expression of the electromagnetic torque
• control schemes (mainly torque and flux control loops)
• operating regions
• estimators (eventually)
  The report has to show the definition of the parameters of the control system and the results of the simulation of the application (Matlab/Simulink)(DdD3, DdD5).

Notes http://castellidezza.faculty.polimi.it/?page_id=10&lang=en
N. Mohan, T.M. Undeland, W.P. Robbins, Power Electronics: Converters, Applications, and Design, Editore: John Wiley & Sons, Anno edizione: 2003
P. Vas, Electrical Machines and Drives, Editore: Clarendon Press, Anno edizione: 1996
Dispense http://castellidezza.faculty.polimi.it/

The course is an advanced module on dynamics and vibrations of mechanical systems, with emphasis on multi degree of freedom systems and distributed parameter systems. First, the techniques for writing the equations of motion of a multi-d.o.f. system are introduced and the solutions for free undamped, free damped and forced motion of the system are derived. Then, beam-type distributed parameter systems are introduced ant their motion is determined under the assumption of small displacements from the un-deformed state. Techniques for the discretisation of distributed parameter systems are also covered by the course with special reference emphasis on the mode superposition approach and on the finite element method. The final part of the course treats the analysis of stability for mechanical systems subjected to field forces, also introducing some examples from real applications.

Lectures and other didactic activities will allow students to:
Understand the dynamics and vibrations of complex mechanical systems (DdD1 - capability of knowledge and understanding, DdD2 - capability of applying knowledge and understanding);
Define mathematical models to describe the motion of mechanical systems formed by rigid and/or flexible bodies (DdD1 - capability of knowledge and understanding, DdD2 - capability of applying knowledge and understanding);
Analyse the stability of mechanical systems, focusing on some significant case studies (DdD2 - capability of applying knowledge and understanding, DdD3 - capability of making judgements);
Summarise and present the results achieved in the analysis of dynamics and vibration problems (DdD3 - capability of making judgements, DdD4 - capability of communication);

1. Dynamics and vibration of single, two and multi-degree-of-freedom systems
1.1 Free and forced vibrations of single-degree-of-freedom linear systems. Natural frequency and damping factor. The Frequency Response Function. The superposition principle and the response to different kinds of input (step, harmonic, periodic, non-periodic).
1.2 Non-linear vibration of single-degree-of-freedom systems. Non-linear motion equation and linearization about a certain equilibrium position. Free non-linear vibration: dependency of the natural frequency on the oscillation amplitude. Forced non-linear vibration: jump phenomenon, subharmonics and combination harmonics.
1.3 Two and multi-degree-of-freedom systems. Non-linear motion equations and linearization about a certain equilibrium position. Free and forced vibrations of two and multi-degree-of-freedom linear systems. Natural frequencies, vibration modes, Frequency Response Function.
1.4 Principal coordinates and modal superposition approach. Modal parameters identification.
2. Vibrations in continuous systems
2.1 Transversal vibrations in stretched strings. Wave equation. Wave propagation solution and standing wave solution. Natural frequencies, vibration modes, forced response.
2.2 Introduction to simple elastic beam models. Euler-Bernoulli beam theory.
2.3 Axial vibrations in beams. Natural frequencies, vibration modes, forced response.
2.4 Torsional vibrations in circular shafts. Natural frequencies, vibration modes, forced response.
2.5 Bending vibrations in slender beams. Natural frequencies, vibration modes, forced response.
2.6 Principal coordinates and modal superposition approach.
3. Introduction to finite element method
3.1 Discretization of the continuous domain into a set of sub-domains: mesh and degrees-of-freedom.
3.2 Euler-Bernoulli beam element: reference systems, degrees-of-freedom, shape functions, inertia and stiffness matrices.
3.3 Modelling of planar beam structures. Coordinate transformation from the local to the global reference system. Assemblage procedure to obtain the matrices of a complete structure. Expression of the nodal forces corresponding to concentrated and distributed loads. Structural damping model.
3.4 Natural frequencies, vibration modes, forced response, motion imposed to the supports.
3.5 Examples and applications.
4. Stability of mechanical systems subjected to non-linear force fields
4.1 Definition of force field. Conservative and non-conservative force fields. Mathematical models of aerodynamic forces and contact forces.
4.2 Stability analysis of single-degree-of-freedom systems, about a certain equilibrium position. Static and dynamic instability.
4.3 Stability analysis of two-degrees-of-freedom systems, about a certain equilibrium position. Flutter instability.
4.4 Examples and applications.
Prerequisites
Handouts
Lecture handouts, training material and proposed exercises will be made available on the course web page.

Prova scritta obbligatoria, con prove in itinere
Prova orale obbligatoria
Progetto/i - Elaborato/i obbligatorio, individuale, senza revisioni periodiche, senza presentazione/discussione finale
The final assessment consists of a written exam, followed by an oral exam.
In the written exam the student will be asked to demonstrate her/his ability regarding the following skills:

• Define a mathematical model for a system of rigid bodies and derive the motion of the system caused by assigned excitation effects (DdD2 - capability of applying knowledge and understanding);
• Analise the vibration of a simple distributed-parameter system (flexible beam) in terms of its natural frequencies, modal shapes and frequency response function for simple inputs like a point force (DdD2 - capability of applying knowledge and understanding, DdD3 - capability of making judgements).
  In the oral exam the student will be asked to demonstrate her/his ability regarding the following skills:
• Describe the motion of systems formed by flexible bodies, with special reference to planar systems of beams (DdD1 - capability of knowledge and understanding, DdD4 - capability of communication).
• Understand the principles underlying the dynamics and stability of mechanical systems (DdD2 - capability of applying knowledge and understanding, DdD4 - capability of communication);
  The oral exam also includes the discussion of a practical case regarding the use of the Finite Element method to analyse the dynamics of a mechanical system (DdD2 - capability of applying knowledge and understanding, DdD3 - capability of making judgements, DdD4 - capability of communication).

Lecture notes
Note: Lecture notes, training material and proposed exercises will be made available on BeeP.
F. Cheli and G. Diana, Advanced Dynamics of Mechanical Systems, Editore: Springer, Anno edizione: 2015, ISBN: 978-3-319-18199-8
L. Meirovitch, Fundamentals of Vibrations, Editore: McGraw-Hill, Anno edizione: 2001
M. Petyt, Introduction to finite element vibration analysis, Editore: Cambridge University Press, Anno edizione: 2010

The general goal of the course is to enable students to extract (“learn”) from measured data useful information for automation, systems and control applications. The course covers the fundamentals of data-driven learning of dynamical systems, with a focus on black-box system identification. Both linear and non-linear systems are considered. Prediction and forecasting are emphasized in time-series analysis and modelling as important applications of system identification. Each technique is treated both theoretically and practically through application examples. Examples of AI-based variable estimation are presented.

Dublin descriptor DD1, Knowledge and understanding

• Through theoretical lectures and numerical exercises sessions, students will achieve a compalgorithms for adaptive filtering, identification and control, the Kalman filter with various extensions, subspace algorithms for state-space identification, ainformation from a finite set of observations taken from an unknown system.
  DD2, Applying knowledge and understanding
• Students will be able to apply the mentioned methods to practical estimation, identification, prediction
  DD3, Making judgements
• Thanks to the project activity, the student will learn to formulate and solve autonomously a practical identification problem,
  DD5, Lifelong learning skills
• Students will be able to apply the methodologies learned throughout the course to tackle complex modeling and estimatread current research papers and understand the issues raised by current research in the field.

The program of the course is as follows.
Discrete-time linear dynamical systems: definition and analysis of model classes for Time Series and Input/Output models (ARMA, ARMAX)
Linear optimal prediction
Black-box identification from measured data-sets of Input/Output models (algorithm, analysis, validation)
Black-box identification from measured data-sets of state-space models
Variable estimation: Kalman Filter and Extended Kalman Filter - Examples of AI-based variable estimation.
Gray-box system-identification
Frequency-domany parametric estimation of models from data
Extension to non-linear dynamical systems AI-techniques

Prova scritta obbligatoria, senza prove in itinere
Prova orale condizionata (a scelta del docente)
Progetto/i - Elaborato/i condizionato (a scelta del docente), di gruppo, senza revisioni periodiche, con presentazione/discussione finale
The exam will be a written test in one of the official available dates. The exam will consist of both theoretical questions and simple (i.e., no computer-aide
In particular, the written exam is designed to assess the following Dublin Descriptors:
DD1 – Knowledge and understanding: students will be asked to demonstrate their understanding of the main problems in data-driven modelinidentification issues.
DD2 – Applying knowledge and understanding: students will be required to select, justify, and apply the most appropriate identification, estim
DD3 – Making judgements: through open-ended questions, students will be evaluated on their ability to critically analyze a given modeling or id
Clarity, rigor of exposition, and proper use of technical terminology will contribute to the final evaluation.

Sergio M. Savaresi, Lecture notes
Sergio Bittanti, Model Identification and Data Analysis, Editore: Wiley, Anno edizione: 2019
M.S. Grewal, A.P. Andrews, Kalman Filtering: Theory and Practice Using MATLAB, Editore: Wiley Online Library, Anno edizione: 2008 http://read.pudn.com/downloads148/ebook/638857/Kalman%20Filtering%20-%20Theory%20and%20Practice%20Using%20MATLAB,%203rd%20Ed.pdf
Note: freely-available online
P. van Overschee, B.L. de Moor, Subspace Identification for Linear Systems: Theory, Implementation, Applications, Editore: Kluwer Academic Publishers ftp://ftp.esat.kuleuven.be/pub/SISTA/ida/reports/96-26a.pdf
L. Ljung, System Identification: theory for the user, Editore: Prentice-Hall
T.S. Soderstrom, P.G. Stoica, System Identification, Editore: Prentice-Hall

Basic principle of Optimal control
Model predictive control (MPC) of linear systems
Stability of MPC
Extensions of MPC
Robust MPC
Basics of nonlinear model predictive control (NMPC)
Numeric Solutions
Basics of Reinforcement Learning (RL)

This module focuses on control methods for systems having multiple inputs and outputs and their application to practically motivated control problems. The module provides an introduction to analysis and synthesis techniques for multivariable control systems. It covers:

• models for linear time invariant multivariable systems (state space models, minimal realisations, observability, controllability, transfer function matrices, coprime polynomial matrix fraction descriptions, generalised polynomial models)
• canonical forms
• poles and zeroes of multivariable systems
• time domain control synthesis methods (pole placement, linear quadatic regulation, observer, observer in the loop)
• frequency domain control synthesis methods (Nyquist array methods, H-infinity methods)

The course objective is to apply the theoretical skills acquired during the study program on a real system, facing and analysing the related issues. The course covers the aspects related to the design and development of an automated system, in terms of system modelling, design of the controller, analysis of the closed-loop system and experimental implementation on a real plant with verification and validation of model and control performance. The topics are treated by means of numerical and experimental applications through a team project work.

With reference to the Dublin Descriptors (DoD1: Knowledge and Understanding; DoD2 Applying Knowledge and Understanding; DoD3 Making Judgements; DoD4 Communication; DoD5: Learning Skills), upon completion of the course t he student:

• [DdD 2] is able to model a complex system
• [DdD 2,5] is able to set up proper experiments and identify missing parameters
• [DdD 2] is able to validate numerical models
• [DdD 2] is able to design and tune control algorithms
• [DdD 2] is able to implement control algorithms on a real-time platform
• [DdD 3,5] is able to critically analyse numerical and experimental results and control performances
• [DdD 4] is able to work in a small team
• [DdD 4] is able to write a technical report
• [DdD 4] is able to present work progress and results

Experiments cover classical control problems for non-linear systems:

• Motion control, comprising experiments on stable and unstable systems;
• Vibration control, co mprising experiments on different types of lumped and distributed flexibility;
• Process control, comprising experiments on level regulation of coupled tanks.
  Each experiment requires system mode l ling, analysis of the sensing and actuation capabilities, identification of the parameters, controller synthesis and testing.
  At the beginning of the course the students will be divided in small teams of 3-4 persons. A single laboratory experience, to be carried out during all the course duration, will be assigned to each team. The attendance to laboratory hours is mandatory. During laboratory hours teachers will support the teams in the development of the experiences, orienting the work progress.

Prova orale obbligatoria
Progetto/i - Elaborato/i obbligatorio, di gruppo, senza revisioni periodiche, con presentazione/discussione finale
During the course the teams are required to perform a short mid-term presentation and should demonstrate the ability to summarise and analyse the work done and the preliminary results.
The final exam is comprised of a technical report and a final presentation to the whole classroom about the project work, to deliver at the end of the course.
In the technical report and during the presentation the students shall demonstrate to:

• [DdD 2] have properly modelled the experience assigned to the team
• [DdD 2] have performed experiments and identified needed parameters
• [DdD 2] have validated the developed model/s
• [DdD 2] have designed, tuned and implemented different control algorithms
• [DdD 2,5] be able to critically analyse numerical and experimental results and control performances
• [DdD 4] be able to summarize and present their work in written and oral form
  The final evaluation will also take into account the student approach to the project work and to the team activities during the laboratory hours. [DdD 3]

K. J. Astrom and R. M. Murray, Feedback Systems An Introduction for Scientists and Engineers, Editore: Princeton University Press, Anno edizione: 2008, ISBN: 9781400828739
G.C. Goodwin, S.F. Graebe, M.E. Salgado, Control System Design, Editore: Prentice Hall, Anno edizione: 2001, ISBN: 9780139586538
R.C. Dorf, R.H. Bishop, Modern Control Systems, Editore: Prentice Hall, Anno edizione: 2010, ISBN: 9780136024583
L. Meirovitch, Fundamentals of Vibrations, Editore: McGraw-Hill, Anno edizione: 2002, ISBN: 9780072881806
L. Meirovitch, Introduction to Dynamics and Control, Editore: John Wiley & Sons, Anno edizione: 1985, ISBN: 9780471822820

The course objective is to apply the theoretical skills acquired during the study programme on a real system, facing and analysing the related issues. The course covers the aspects related to the design and development of an automated system, in terms of mechanical/electrical/pneumatic/hydraulic system modelling, selection and positioning of sensors for control loop closure, selection of suitable control logics, control-system modelling, numerical simulation and experimental implementation of the control system on a real plant, verification and validation of models and control logic performances. The topics are treated by means of numerical and experimental applications through a team project work.

With reference to the Dublin Descriptors (DoD1: Knowledge and Understanding; DoD2 Applying Knowledge and Understanding; DoD3 Making Judgements; DoD4 Communication; DoD5: Learning Skills), upon completion of the course t he student:
[DdD 2] is able to model a complex system
[DdD 2,5] is able to set up proper experiments and identify missing parameters
[DdD 2] is able to validate numerical models
[DdD 2] is able to design and tune control algorithms
[DdD 2] is able to implement control algorithms on a real-time platform
[DdD 3,5] is able to critically analyse numerical and experimental results and control performances
[DdD 4] is able to work in a small team
[DdD 4] is able to write a technical report
[DdD 4] is able to present work progress and results

Methodologies for automated system modelling. Schematisation levels: selection criteria on the basis of the system typology, of the operating conditions, of the working frequency range. Simulation environments.
1. Process simulation and control. Design of process control systems alternatively for the level regulation of coupled tanks or for temperature control of an heat-flow system. Modelling of the systems and linear approximation for control, selection and positioning of the sensors for control loops closure, selection of suitable control logics, compilation of the simulation models for the system to be controlled and for the control system, accounting for the dynamic aspects that may reduce the closed-loop performances (couplings, non-linearities, actuation delays), dynamic simulation in open and closed loop, possible redesign for maximising the system performances.
2. Electrical drives. Survey of electrical drives' components and of of electrical machines' and static converters' performances, definition of DC and AC electrical drives' structure. Experimental evaluation of the issues related with electrical actuators' control, by using standard industrial components and/or open an programmable systems.
3. Discrete event systems. Modeliling and control of discrete event systems. Definition of PLC structure and programming languages. Laboratory experiences on programmable logic controllers (PLCs), field buses and pneumatic circuits.
4. Active control of mechanical systems. Modelling, testing and optimisation of control systems for motion control and vibration control of mechanical systems with lumped and distributed flexibility. Mechanical system modelling, selection of the sensor position, of the actuation system and of the control law.
At the beginning of the course the students will be divided in small teams (3-4 persons). A single laboratory experience, to be carried out during all the course duration, will be assigned to each team. The attendance to laboratory hours is mandatory. During laboratory hours teachers will support the teams in the development of the experiences, orienting the work progress.

Progetto/i - Elaborato/i obbligatorio, di gruppo, con revisioni periodiche, con presentazione/discussione finale
Valutazione continua obbligatoria
The final exam is made of the evaluation of a technical report and a final presentation to the whole classroom of the project work.
In the technical report and during the presentation the students shall demonstrate to:
have properly modelled the experience assigned to the team [DdD 2]
have performed experiments and identified needed parameters [DdD 2]
have validated the developed model/s [DdD 2]
have designed, tuned and implemented different control algorithms [DdD 2]
be able to critically analyse numerical and experimental results and control performances [DdD 2, 5]
be able to summarize and present their work in written and oral form [DdD 4]
The final evaluation will also take into account the student approach to the project work and to the team activities during the laboratory hours. [DdD 3]

K. J. Åström and R. M. Murray, Feedback Systems, Editore: Princeton University Press, ISBN: 9780691135762
G.C. Goodwin, S.F. Graebe, M.E. Salgado, Control System Design, Editore: Prentice Hall, Anno edizione: 2001, ISBN: 9780139586538
R.C. Dorf, R.H. Bishop, Modern Control Systems, Editore: Prentice Hall, Anno edizione: 2010, ISBN: 9780136024583
P. Vas, Electrical Machines and Drives, Editore: Clarendon Press, Anno edizione: 1993
W. Leonhard, Control of Electrical Drives, Editore: Springer, Anno edizione: 2001
L. Meirovitch, Fundamentals of Vibrations, Editore: McGraw-Hill, Anno edizione: 2002, ISBN: 9780072881806
L. Meirovitch, Introduction to Dynamics and Control, Editore: John Wiley & Sons, Anno edizione: 1985, ISBN: 9780471822820

Up to four students cooperate in solving a project task that is related to current research questions of control engineering, rehabilitation robotics, or neuroprosthetics. Unlike common labs with predefined subtasks, this project lab requires each participant to assume full responsibility of the entire project and its success. The workload as well as major decisions are shared, but the project is managed by one of the group members. This is common practice in industrial team work environments. While the goals of each project are precisely described by the adviser, there are typically no predefined solution paths. All required devices, material, and data sheets are provided. In regular meetings, advice is given on how to manage the project and how to achieve the project goals.

Software development is more and more important in the realization of modern control systems. This course provides students in automation the basic concepts for a rigorous development of software systems. The course will introduce several specification notations and the most used methods for software verification and validation. Finally, it will present some examples of software development tool chains through a pair of case studies.

Dublin Descriptors
Expected learning outcomes
Knowledge and understanding (DdD 1)
Students will learn how to:
identify the phases of software development and the specific activities associated with each phase
model the behavior of software applications before they are implemented, in particular for what concerns functional and nonfunctional aspects
define the structure of software systems in terms of their modules and the interactions among them
verify and validate software systems to increase the level of quality
apply manual and automated testing techniques to find software defects
Capability to apply knowledge and understanding (DdD 2)
Given informal descriptions of specific software applications, students will be able to:
build precise models of their desired behavior, to better understand the involved problems
define test cases to verify that the implemented application works as desired
(for students who take the exam through a project) develop and test code that realizes the desired application
Communication skills (DdD 4)
Students who take the exam through a project will learn to:
produce rigorous specifications of the work they carried out
present their work to the teacher, explain and defend their design choices
Learning skills (DdD 5)
Students who take the exam through a project will learn to find solutions – in full autonomy – to the unforeseen problems that arise in the development of new applications

1. General principles of software engineering.
1.1 The qualities of software.
1.2 Basic principles of software engineering.
1.3 A brief summary of the software lifecycle.
2. The modular design of software.
2.1 UML-based design.
2.2 General principles of object-oriented design and implementation.
3. Notations for the specification of software.
3.1 Operational notations: finite-state automata, Statecharts.
3.2 Descriptive notations: logic specifications.
4. Software verification through testing.
4.1 Basic principles and definitions.
4.2 White-box testing: coverage criteria.
4.3 Black-box testing: through specifications of programs (pre- and post-conditions).
4.4 Automated testing: symbolic execution, fuzzing, search-based software testing.
5. Examples of software development toolchains
Exercise work
Part of the classes will focus on presenting and solving exercises concerning topics covered during lessons.

Prova scritta facoltativa (a scelta dello studente), senza prove in itinere
Progetto/i - Elaborato/i facoltativo (a scelta dello studente), di gruppo, senza revisioni periodiche, con presentazione/discussione finale
The assessment is based on a written exam at the end of the course. The written exam is open book, and it will assign up to 32 points. 30 cum laude will be assigned when the total score is higher than 31.
Students can also opt to pass the exam by developing a software project (from requirements, down to implementation and testing), in which they are expected to apply in practice the principles and techniques described during classes. The software development project must be carried out in a team of 2 or 3 people. Students who develop the software project are asked to produce documentation concerning the application developed and must present their work in a final oral exam.
The following table provides a detailed overview of the elements that will be considered in the various assessment activities.
Type of assessment
Description
Dublin descriptor
Written test
Solution of numerical problems
Software testing and anlaysis exercises
Exercises focusing on design aspects
Definition of UML models from informal descriptions
Definition of formal models of behaviors informally described, using various formalisms
DdD 1, 2, 5
Assessment of laboratorial artifacts
Assessment of the design and implementation work carried out by students in groups
DdD 2, 4, 5
Oral presentation
Assessment of the presentation of the work carried out by students in groups during the software development project
DdD 2, 4, 5

Camilli, M., Di Nitto, E., Fuggetta, A., Margara, A., Rossi, M., Tamburri, D.A., SOFTWARE ENGINEERING - A Structured Journey from Inception to Delivery, Editore: Bocconi University press, Anno edizione: 2025, ISBN: 9791280623508

Processi di Lavorazione Additivi
La produzione mediante processi tradizionali di lotti poco numerosi o al limite unitari di prodotti con geometrie ad alta complessità è sempre stata considerata come economicamente svantaggiosa. La situazione sta ora modificandosi grazie all'introduzione dei processi basati su tecnologie additive (Additive Manufacturing - AM), spesso comunemente note come tecnologie di stampa 3D. Dopo essere stati utilizzati per più di vent'anni nell'ambito dei processi di sviluppo prodotto come tecnologie per la fabbricazione rapida di prototipi, utensili e attrezzature, le tecnologie additive sono sempre più utilizzate per la produzione di singoli prodotti o prodotti personalizzati in molti settori industriali. Il corso fornirà un'introduzione alle tecnologie di produzione additiva, discutendone le loro applicazioni e le implicazioni tecniche e gestionali in riferimento a tutti i possibili utilizzi di tale innovativa tecnologia di produzione.

Overview of the control hierarchy from business systems to basic control loops. Design of control structures and strategies for large plants. Commonly used controller functions in basic control loops. Analysis and compensation of interactions in multivariable systems. Analysis of limitations in achievable performance. Applied model-based estimation and predictive control (MPC). Control performance monitoring and diagnosis. The projects are mainly developed in cooperation with Norwegian oil and gas companies.

Big data and IOT are crucial concepts in the Industry 4.0 framework and the rapid evolution of measurement instrumentation is changing how measurements are performed. The recent developments in the field of sensors enable the description of physical phenomena at an incredibly low cost. As a matter of fact, the developments of novel sensors, data acquisition techniques, and data analysis procedures imply the necessity of deep metrological knowledge for an automation engineer.
The teaching aims at helping the students in the selection of the proper transducers for mechanical and thermal control applications, by describing the main categories of mechanical and thermal instrumentation from both a theoretical and a practical point of view. In addition to a description of the sensors that nowadays can be used for different applications, the teaching will focus on identifying the generic properties of instruments that can be used to select and compare the optimal instrumentation and to design smart sensing infrastructures. In this way, the student will be prepared to face the rapid evolution that characterizes the world of metrology in the industry 4.0 era.

At the end of the teaching, the student is expected to reach the following learning outcomes
Knowledge and comprehension (Dublin Descriptor 1, DD1)
Traceability and calibration
measurement uncertainty
Metrology
Ability in the application of knowledge (DD2)
prepare a setup for the acquisition of data coming from the most common measurement instruments
compute the measurement uncertainty in accordance with the ISO GUM
to design fit-to-purpose measurement chains for specific mechanical and thermal applications
Authonomy in judgement (DD3)
select the best trasducer for the majority of mechanical and thermal measurements.
compare two similar transducers by analyzing their datasheet and their metrological characteristics
identify the correct calibration procedure for the majority of mechanical and thermal sensors
Communication skills (DD4)
Students that decide to present the project instead of the written exam will learn how to work in team and how to present the results of a project
All the competences listed above will be developed relying on the theoretical lectures and on the laboratory experiences, that are crucial for a deep understanding of the most common problems that have to be faced during the automation engineer working life.

Generalized performances of measurement instruments
Uncertainty; Static and dynamic performances of measurement instruments; generalized theory of measurement instruments; load effect.
Fourier Analysis; dynamic performances of measurement systems; time response of first- and second-order systems. Experimental identification of response of first- and second-order systems; edge computing and smart sensing infrastructures.
Strain measurements
Introduction to strain gauges; calibration, Wheatstone bridge measurement circuits; compensation of thermal effects and improvement of sensitivity.
Vision-based sensors
Non-contact optical transducers: Laser triangulation sensors; Time of Flight lasers and Cameras, Laser Doppler vibrometers and interferometers. Vision-based systems and machine vision. Cameras for 2D and 3D machine vision applications; most common algorithms for machine vision
Non-contact temperature measurements
Rapid recall to thermal resistances, thermistors, thermocouples. Infra-red sensors: theoretical principles. Pyrometers, dual-color pyrometer. IR cameras. Experimental identification of emissivity
Each topic is treated theoretically in a classroom lecture. Students can use the different measurement instruments and analyze the experimental data according to the procedures learned during the theoretical lectures in the following laboratory experience.
There will be a relevant collaboration with company leaders in the field of measurement for automation. One or more companies will provide assignments to students, that will use the knowledge developed during the course to propose a solution, that will be presented to the company during a pitch.

Prova orale obbligatoria
Progetto/i - Elaborato/i obbligatorio, di gruppo, senza revisioni periodiche, con presentazione/discussione finale
The exam is a Project-Based Assessment
1. Project Assignment

• Each student or group of students is assigned a project topic related to measurement systems and data analysis.
  2. Laboratory Component
• Students will perform supervised or unsupervised experiments in lab settings, using Polimi instrumentation, to to collect data relevant to their project topic. Students are required to record data and troubleshoot normal issues that arise during experiments.
  3. Independent Research and Analysis:
• In supervised classroom labs, students will prepare the groundwork of the project; students are expected to apply theoretical concepts learned in the course to interpret experimental results and draw meaningful conclusions from their analysis.
  4. Project Presentation
• Students will prepare a presentation of their work and a short report. Presentations should be based on slides, graphs, and charts.
  5. Evaluation Criteria:
  Criterion
  Points
  Description & Dublin Descriptors (DoD)
  Quality of Work
  20 pts
  Evaluation of the technical solution and the ability to apply acquired knowledge. Includes the intelligent and balanced use of AI tools in document preparation.
  DoD: Applying knowledge and understanding and Making judgements (critical selection of tools and solutions).
  Presentation Skills
  5 pts
  Assessment of delivery clarity, the quality of slides in transferring key concepts, and strict adherence to the 15-minute time limit.
  DoD: Communication skills.
  Response to Questions
  5 pts
  Evaluation of the student's ability to defend their work and apply their knowledge to specific inquiries.
  DoD: Applying knowledge and understanding and Learning skills.

Motivated by the increasing penetration of distributed energy resources (DERs) and the structuring of the electrical grid into multiple microgrids that enable the provision of new smart grid services, the goal of this course is to introduce students to strategies for the optimal coordination and control of complex systems coping with aspects like a multi-agent large scale structure, the presence of uncertainty affecting the system and of integer decision variables, and the need to guarantee privacy of local information.

The students will get some insight into the electrical grid evolution and learn methods for modeling typical energy management problems and mastering their complexity through optimization-based approaches for the operation of multi-agent possibly uncertain systems (DdD1 - Knowledge and understanding). Through the project work, they will not only apply the acquired knowledge (DdD2 - Capability to apply knowledge and understanding) but also improve their autonomy and critical thinking (DdD3 - Making judgements), their team working and communication skills (DdD4 - Communication skills), and their capability of devising ad-hoc solutions to the problem at hand (DdD5 - Learning skills).

1. Introduction to the electrical grid system and to the challenges and opportunities offered by its current evolution towards the smart grid concept. In particular, electrical energy production from traditional and renewable sources will be considered, as well as requirements in various use cases and scenarios, role of non-programmable sources and impact on the network infrastructure, with a special focus on technological trends and developments of DERs and microgrids, including storage, electric vehicles, and ICT technologies.
2. Description of some relevant case studies for the optimal management of the electrical grid that can be formulated in terms of constrained optimization problems possibly involving multiple agents and affected by uncertainty, calling for distributed and data-driven resolution approaches. In particular, we shall consider the provision of ancillary services to the grid by the aggregation of multiple prosumers, the scheduling of the charging of a fleet of electric vehicles, and the energy management of a building district.
3. Motivated by the above case studies, we shall present relevant algorithms for solving multi-agent convex optimization problems with coupling due to shared decision variables or resources (the distributed dual subgradient and alternating direction method of multipliers algorithms), and discuss their extension to address the case when integer variables and uncertainty known only from data are present.

Prova orale obbligatoria
Progetto/i - Elaborato/i obbligatorio, di gruppo, senza revisioni periodiche, con presentazione/discussione finale
The final assessment will be based on

• an oral exam on the content of the course (DdD1, DdD4);
• a report and a presentation on a project work carried out by the students in small groups related to the presented case studies (DdD2, DdD3, DdD4, DdD5).

Slides of the course and some relevant papers https://webeep.polimi.it/

Physical processes involving fluid flow, heat transfer and thermal/mechanical power conversion are at the heart of numerous engineering systems, whose performance crucially depends on control. The main goal of the course is to provide the methodological foundations for the control-oriented dynamic modelling of such systems, enabling the students to understand what are the control-relevant phenomena, how to model them and how to use these models to design effective control strategies. Particular emphasis is put on the concept that the achievable control performance is determined by the dynamic physical behaviour of the process and on establishing links between physical process behaviour and control-relevant aspects.
The concepts are applied to a number of examples in the field of process control and thermal power generation, thus also providing the students an overview of typical control problems in this field and on how they can be solved effectively once the dynamic process behaviour has been clearly analyzed. The course mostly focuses on cases that can be analyzed with simple, closed-form analytic models, but also briefly discusses the role of numerical modelling and simulation for more complex cases.

Knowledge and understanding (Dublin descriptor DD1). The student:
knows the basic conservation equations governing thermo-mechanical-hydraulic processes, both lumped-parameter (0D) and distributed-parameters (1D)
knows the constitutive equations of the models of specific phenomena (such as pressure losses or heat transfer) as well as of specific machinery (such as valves, turbines, compressors, etc.)
understands the time scale of different dynamic phenomena in these processes
is aware of how classical control problems are solved in the field of fluid processing and thermal power generation
understands the role of simplified analytical control-oriented models and of more detailed numerical simulation models for the design of control systems
understands how the mechanical design parameters and the operating point of such systems influence their controllability and how it may be possible to improve the control performance also by changing these parameter (process-control co-design)
understands what are the main control problems in a number of applications in the field of fluid processing and thermal power generation and which model-based control strategies can be used to solve them
Capability to apply knowlegde and understanding (Dublin descriptor DD2). The student is able to
estimate the time scale of different dynamic phenomena in thermo-hydraulic-mechanical processes, so as to select the ones which are relevant for control while neglecting those which are not
derive control-oriented dynamic models of thermo-hydraulic-mechanical processes
analyze these models and simplify them as much as possible to gain insight on the relationship between the mechanical design and operating parameters, the control design, and the achievable control performance
use the information gathered from the analyis of the system dynamics to determine the structure/architecture of effective control systems
use appropriate numerical methods to extract control-relevant information from detailed numerical simulation models of the process
interact with specialists in the area of thermo-hydraulic-mechanical process design in multi-disciplinary teams with the ultimate goal of designing better-controlled innovative systems
Judgement and autonomy (Dublin description DD3): The student is able to analyze thermo-hydraulic system dynamics, making educated guesses of unknow data and identifying critical missing data about the process.
Communication (Dublin descriptor DD4). The student is able to effectively present the results of a case study where the concepts, models and methods learned in class were applied to the design of an actual control system, whose performance can be analyzed by means of simulations.

Part 1: Introduction and fundamental equations
Role of modelling for the design of control systems
Detailed models for numerical simulation vs. simplified analytical models for control design
Mass, energy and momentum balance equations for 0D systems
Mass, energy and momentum balance equations for 1D systems
Estimation of time scales of different phenomena in 1D systems
Equations of state and thermodynamic properties of working fluids
Part 2: Modelling of components of thermo-hydraulic and power production processes
Control valves and piping equipment
Turbomachinery: pumps, compressors, turbines
Simplified models of combustion phenomena
Single-phase heat exchangers
Two-phase heat exchangers and boilers
Electrical power generation and transmission equipment
Part 3: Analysis and design of control systems
Control of simple hydraulic circuits
Power/frequency control in hydro power plants
Temperature control in single-phase heat exchangers
Control pressure, level and load in simple steam generators
Control problems in coal-fired and combined-cycle power plants
Numerical methods to extract control-relevant information from numerical models
Applications in the control of innovative power generation systems

Prova scritta obbligatoria, senza prove in itinere
Prova orale facoltativa (a scelta dello studente) o condizionata (a scelta del docente)
Progetto/i - Elaborato/i obbligatorio, di gruppo, senza revisioni periodiche, con presentazione/discussione finale
The final learing assessment is by written exam, with optional oral discussion to change the final mark up to +/- 3 points. During the exam, the student will be asked to
Write down appropriate conservation laws and consitutive equations for all the system components described in the course (DD1)
Evaluate the time scale of different physical phenomena in a given process and, based on that, decide what are the most appropriate modelling assumptions for a control-oriented model (DD2)
Derive simplified models of the fundamental dynamics of a given process, that can be used for control design, and use them to derive a control strategy and possibly to tune the controller parameters (DD2)
Describe and discuss the control-relevant dynamic phenomena, the control problems and the control strategies of the systems discussed during the course (DD1, DD4)
The student will also be asked to hand in a home assignment, where he/she will be asked to design and test the controller of a specific process (DD2, DD4). A detailed simulation model of the process will be provided, as well as an open-source environment to run it.

Lecture notes
G. Quazza, Controllo dei processi, Editore: Clup, Anno edizione: 1976
R. Dolezal, L. Varcop, Process Dynamics - Automatic Control of Steam Generation Plant, Editore: Elsevier, Anno edizione: 1970
C. Maffezzoni, Dinamica dei generatori di vapore, Editore: Masson, Anno edizione: 1989
C. Maffezzoni, Controllo dei generatori di vapore, Editore: Masson, Anno edizione: 1990
F. Saccomanno, Electric Power Systems: Analysis and Control, Editore: Wiley Interscience, Anno edizione: 2003
S.G. Dukelow, The Control of Boilers, Editore: ISA, Anno edizione: 1991

L'obiettivo di questo corso è fornire agli studenti conoscenze su argomenti avanzati, sia metodologici che tecnologici, rilevanti per l'ingegneria dell'automazione.

Aerial robots, such as multirotor UAVs, have emerged as one of the most versatile robotic platforms, enabling applications in inspection, monitoring, logistics, and physical interaction with the environment. Their increasing diffusion and complexity call for a deep understanding of modeling, estimation, control, and interaction principles that underpin their operation.
The goal of this course is to provide students with fundamental knowledge and practical skills in aerial robotics, combining theoretical foundations, algorithmic implementation, and hands-on experimentation in simulation. The course adopts a three-pillars approach:
Models and theoretical principles
Computational tools and simulation environments
Real-world inspired applications
Through lectures, guided exercises, and a final project, students will learn to model aerial robots, design estimators and controllers, generate trajectories, and study physical interaction strategies. Modern approaches integrating optimization and AI for aerial robotics will also be introduced. The course emphasizes both analytical understanding and practical implementation, aiming to equip students with skills directly transferable to research and industry.
The course will be entirely delivered by Dr. Marco Tognon, from INRIA Rennes, France.

1. Knowledge and understanding (Dublin Descriptor 1, DD1)
Upon completion of the course, students will:
understand the modeling principles of multirotor UAVs (underactuated and fully actuated);
have basic knowledge on how to design and evaluate state estimators (Kalman filter, UKF, particle filters) for aerial platforms;
understand the fundamentals of control for stabilization, trajectory tracking, and aggressive maneuvers;
grasp the principles of physical interaction through admittance and impedance control;
recognize the role of optimization and AI-based methods in modern aerial robotics.
2. Capability to apply knowledge and understanding (DD2)
After lectures and exercises, students will be able to:
create simulation models of UAVs in ROS/Gazebo and analyze their behavior under different control inputs;
implement state estimation methods and validate them against simulation data;
design and tune controllers for trajectory tracking and interaction tasks;
solve practical problems such as waypoint navigation, trajectory generation, and force-based interaction;
integrate optimization and learning-based strategies in UAV control design.
3. Autonomy (DD3)
Students will gain the ability to:
analyze an aerial robotics task and translate it into a structured control and estimation problem;
critically compare different modeling, estimation, and control strategies;
assess the quality of simulation results and identify possible improvements.
4. Communication skills (DD4)
Through project reports and presentations, students will develop skills to:
clearly present the problem addressed and the adopted approach;
effectively communicate modeling and implementation choices;
discuss results in a concise and technically rigorous way.
5. Learning skills (DD5)
The course will enable students to:
independently learn advanced methods for trajectory optimization, cooperative aerial robotics, or perception-driven flight control;
critically read and evaluate research papers and open-source frameworks in aerial robotics.

Part I: Introduction
Motivation and applications of aerial robotics.
Simulation tools for robotics: Gazebo and Python Genomix.
Hands-on: modeling and controlling simple robots in Gazebo.
Part II: Modeling
Dynamics of multirotor UAVs (underactuated and fully actuated).
Allocation matrix, force envelopes, and actuation limits.
Exercises in Gazebo: underactuated vs. fully actuated behavior, pendulum model, and rotor velocity tests.
Part III: State Estimation
Measurement models and observability.
Basic concepts of Linear Kalman filter, Unscented Kalman filter, and Particle filters.
Exercises: estimator implementation and validation with simulation data.
Part IV: Control
Fundamentals of stabilization and trajectory tracking.
Controllers for underactuated and fully actuated UAVs.
Exercises: gain tuning, trajectory tracking (circles, eights, flips).
Part V: Trajectory Generation
Basics of trajectory planning and waypoint navigation.
Exercises: take-off, waypoint following, simple maneuvers.
Part VI: Physical Interaction
Force-based interaction principles.
Admittance control, impedance control, and wrench observers.
Exercises: gain tuning, pushing tasks, interaction with objects.
Part VII: Advanced Topics
Optimization for aerial control.
AI-based methods for aerial physical interaction.

Prova scritta obbligatoria, senza prove in itinere
Progetto/i - Elaborato/i facoltativo (a scelta dello studente), di gruppo, senza revisioni periodiche, con presentazione/discussione finale
Students will be evaluated with a written exam (DD1, DD2, DD4), with a mix of open questions and multiple choices. The maximum grade achievable with this option is 30/30. Students will also have the possibility to carry out a project (not mandatory) in groups, culminating in a demo (DD3, DD5). The project will include problem formulation, model setup, implementation, and validation through simulation. The project will result in maximum 3 additional points on the grade of the written exam. The maximum grade achievable with both the written exam and the project is 30L/30.

The objective of the course is the development of a general and specific know-how on the estimation and control methods for the autonomous navigation of road vehicles. The course focuses on ground vehicles and, more specifically, on passenger cars and alike. The course deals with the fundamental problems of trajectory and speed-profile generation, trajectory-tracking, positioning and obstacle-detection, and provides an overview of the main sensing and SW-sensing technologies in autonomous cars.
The course aims to illustrate the basic sub-problems and technologies needed to develop a completely-autonomous road vehicle, with special attention on the estimation issues, and on the control strategies and algorithms.
The main focus will be on fully-autonomous vehicles (L4 and L5), but also conditional autonomy (L3) will be considered, with attention on human-factors (disengagement and re-engagement of human driver).
The course will present the classical sub-topics of autonomous vehicle design:

• Sensing and perception, estimation, localization
• Global planning
• Local planning
• Trajectory tracking
  These topics will be developed at different levels of detail, where the emphasis will be on estimation and control algorithms specific for road vehicles (passenger cars or alike). The case of extreme performance (driving at the very limit of the car), in a in-circuit environment will be also considered.

Dublin Descriptor
Expected Learning Result (Specific to the Course)
Knowledge and understanding
Understand the fundamental problems, methods, and technologies for autonomous vehicle navigation (L3-L5), with a specific focus on estimation issues and control strategies (e.g., trajectory generation, localization, obstacle detection).
Applying knowledge and understanding
Design and apply control and estimation algorithms to solve specific sub-problems in autonomous navigation, such as trajectory-tracking, local planning, and vehicle control at the limits of handling.
Making judgements
Critically analyze and evaluate trade-offs between different design solutions, control algorithms, and sensing technologies for autonomous passenger cars, including considerations for human-factor issues in L3 systems.
Communication skills
Communicate a complex control-design solution for a specific autonomous vehicle application (e.g., path following, speed profiling) in a synthetic and technically precise way.
Learning skills
Autonomously read, interpret, and understand specialized industrial technical documentation and research papers in the field of autonomous vehicle control and estimation.

The course will cover the main areas of estimation and control technologies for autonomous cars (sensing and localization, global and local planning, trajectory-tracking and HMI). More specifically, the course sub-topics are the following:
Overview of “ego” sensing technologies; basics of their signal-processing
Road-sensing and vehicle-states sensing for control design: estimation of road-tire available friction, lateral and longitudinal slips (used for local planning and trajectory tracking)
Map-based positioning using GNSS, IMU and road-markers
Trajectory tracking (lateral, longitudinal, and joint lateral-longitudinal).
Local planning: control-oriented generation of trajectories (obstacle-avoidance trajectories)
Global planning: control-oriented generation of trajectories (in-circuit optimal trajectories) and optimal speed-profile (for energy, comfort, speed optimization)
Partially/conditioned autonomy and human factors

Prova scritta facoltativa (a scelta dello studente), senza prove in itinere
Prova orale condizionata (a scelta del docente)
Progetto/i - Elaborato/i facoltativo (a scelta dello studente), di gruppo, senza revisioni periodiche, con presentazione/discussione finale
The exam will be a written test in one of the official available dates or a project (group or single). The exam consists of questions on the topics presented during the course. The project requires the development of techniques starting from the one seen in class.
Grading will be on the following basis: either 100% final project (group presentations with discussion with instructor) or 100% written test on the course topics.
The instructor reserves the right to hold an oral examination in cases where the written test does is not definitive in assessing the student's knowledge.
Type of assessment
Description
Dublin descriptor
Final project
Students are required to:
show their understanding of the main problems (and the corresponding solutions) in the field of control-systems design in vehicles;
use the knowledge acquired in the course to solve problems that they have not be exposed to in class.
select and describe the most appropriate tools for a given application;
be able to communicate in a oral presentation the solution to a specific application problem (modelling or analysis or control problem).
implement the solution they propose.
1,2, 3, 4, 5
Written test
show their understanding of the main problems (and the corresponding solutions) in the field of control-systems design in vehicles;
use the knowledge acquired in the course to solve problems that they have not be exposed to in class.
select and describe the most appropriate tools for a given application;
be able to communicate in a mixed text, graphical and mathematical way the solution to a specific application problem (modelling or analysis or control problem).
1, 2, 3, 4, 5

Matteo Corno, Lecture notes

L'obiettivo di questo corso è quello di sviluppare dei casi di studio ("end-to-end") di progettazione di sistemi di controllo, con riferimento ai principali problemi di controllo della dinamica del veicolo. I casi di studio si concentrano su veicoli terrestri, con particolare riferimento alle automobili o veicoli simili. Viene data enfasi al caso in cui il veicolo è dotato di trazione elettrica o ibrida (ovvero nel quale uno o più motori elettrici possono essere utilizzati come attuatore per il controllo della dinamica del veicolo, ed i problemi di dinamica del veicolo si accoppiano a problemi di gestione e ottimizzazione dell'energia)

The general goal of the course is to enable students to learn the general method and the basic steps of the design of automatic control systems:

• Definition and discussion of a specific control problem
• Selection and location of sensors and actuators
• Development of a simplified (control-oriented) model of the system, aimed at the development and analysis of control algorithms,
• Identification of the model parameters from measurements on the system, and study of the dynamic characteristics of the model
• Design and analysis of control algorithms
• Implementation of the control algorithm
  This approach will be learnt using a “case study” approach on a set of specific application cases. The application cases illustrated in the course are a set of important control problems in the field of vehicles-dynamics and energy-management.

Dublin Descriptor
Expected Learning Result (Specific to the Course)
Knowledge and understanding
Understand the fundamental problems, methods, and technologies for vehicle-dynamics control, with a specific focus on estimation issues and control strategies.
Applying knowledge and understanding
Design and apply control and estimation algorithms to solve specific sub-problems in vehicles dynamics control, such as vertical dynamics control, longitudinal, and laterla dynamics, control, and energy optimization.
Making judgements
Critically analyze and evaluate trade-offs between different design solutions, control algorithms, and sensing technologies for passenger cars.
Communication skills
Communicate a complex control-design solution for a specific autonomous vehicle application in a synthetic and technically precise way.
Learning skills
Autonomously read, interpret, and understand specialized industrial technical documentation and research papers in the field of vehicle control and estimation.

The course consists of several case studies of real, significant problems related to automation in transport systems and vehicles:

• Control of the vertical, roll and pitch dynamics through “intelligent” electronic suspensions systems (load-levelling, active semi-active suspensions, etc.), with emphasis on electric cars
• Control of the longitudinal dynamics during braking and acceleration (ABS, traction control, Adaptive Cruise Control, Control of longitudinal dynamics), with emphasis on electric and hybrid cars (electric motors are used as actuators in braking and traction control)
• Control of the yaw dynamics (Stability Control Systems; Control of lateral dynamics) with emphasis on electric and hybrid cars (electric motors are used as actuators in braking and traction control
• Hybrid-powertrain architectures, and fundamentals of combined control of longitudinal dynamics and energy-management in "green" hybrid and electric vehicles
• Analysis and discussion of future trends of mobility, towards safer and low-emissions green mobility

Prova scritta obbligatoria, senza prove in itinere
Prova orale condizionata (a scelta del docente)
Progetto/i - Elaborato/i condizionato (a scelta del docente), di gruppo, senza revisioni periodiche, con presentazione/discussione finale
The exam will be a written test in one of the official available dates or a project (group or single). The exam consists of questions on the topics presented during the course. The project requires the development of techniques starting from the one seen in class.
Grading will be on the following basis: either 100% final project (group presentations with discussion with instructor) or 100% written test on the course topics.
The instructor reserves the right to hold an oral examination in cases where the written test does is not definitive in assessing the student's knowledge.
Type of assessment
Description
Dublin descriptor
Final project
Students are required to:
show their understanding of the main problems (and the corresponding solutions) in the field of control-systems design in vehicles;
use the knowledge acquired in the course to solve problems that they have not be exposed to in class.
select and describe the most appropriate tools for a given application;
be able to communicate in a oral presentation the solution to a specific application problem (modelling or analysis or control problem).
implement the solution they propose.
1,2, 3, 4, 5
Written test
show their understanding of the main problems (and the corresponding solutions) in the field of control-systems design in vehicles;
use the knowledge acquired in the course to solve problems that they have not be exposed to in class.
select and describe the most appropriate tools for a given application;
be able to communicate in a mixed text, graphical and mathematical way the solution to a specific application problem (modelling or analysis or control problem).
1, 2, 3, 4, 5

Sergio M. Savaresi, Lecture notes
Savaresi S.M., C. Poussot-Vassal, C. Spelta, O. Sename, L. Dugard, SEMI-ACTIVE SUSPENSION CONTROL FOR VEHICLES, Editore: Elsevier (Butterworth-Heinemann), UK
Savaresi S.M., M. Tanelli, ACTIVE BRAKING CONTROL SYSTEM DESIGN FOR VEHICLES. 1st Edition, Editore: Springer-Verlag
Boniolo Ivo, S.M. Savaresi, Estimate of the lean angle of motorcycles, Editore: VDM Verlag, Germany

Systems for the production, distribution and use of energy are becoming more and more complex and articulated. As a result, their level of automation is increasing. This involves virtually any component of such systems, from production plants to transmission, distribution and district networks, down to single units. Also, modern energy systems need to integrate with one another to improve efficiency. And finally, aspects such as comfort, economy and environmental impact, are steadily gaining importance.
The above said, the aim of the course is to address the scenario above by providing the student a system-level view - which is typical of the automation engineer - on the matter, the control problems encountered, the solutions adopted to date, and their possible future developments. The course does not delve into details on the various types of generators, utilisers and so forth, as the said details that can be learnt in specialised courses; for example, power generation processes are addressed in Advanced Process Control.

Knowledge and understanding
The student will understand the basic principles behind the operation, control and management of energy systems, with particular reference – as paradigmatic examples – to electric and thermal networks.
He/she will also master the main control structures, including optimisation-oriented ones where applicable, devoted to the automation of such systems.
Furthermore, the student will acquire knowledge about control-oriented modelling principles, with emphasis on system-level representations based on first-principle approaches.
Applying knowledge and understanding
The student will be able to apply the above principles and methods to

• develop simplified yet physically meaningful models of energy systems for control purposes;
• synthesise and assess control structures suited to such models, using the proper analysis and simulation tools;
• carry out and document small-scale projects involving control-oriented modelling and control design for electric and heat networks.
  Making judgements
  By the end of the course, the student will be capable to
• evaluate the suitability of different control architectures and strategies for specific energy-system scenarios;
• critically assess modelling assumptions and their implications for control performance;
• interpret and discuss the results of simulation studies and laboratory activities, identifying limitations and possible improvements.
  Communication
  The student will learn to present control-oriented studies and designs effectively, through concise technical reports compliant with academic and professional standards.
  He/she will also develop the ability to discuss about automation in energy systems with clarity, using the proper technical terminology.

Introduction. This part quickly provides a high-level view of the matter. It also introduces the basic concepts related to energy systems, generalities, and the corresponding terminology.
Control structures for energy systems. The main control structures used in energy systems are discussed. These include feedforward compensation, cascade control, decoupling, Smith predictor, Internal Model Control, override, and ratio control. The typical uses of such structures in energy systems is discussed. Care is taken to orient their choice and composition based on the characteristics of the encountered dynamic systems rather than (only) on the physical nature of the controlled variables. Most relevant, the logic associated with the addressed structures (interlocks, auto/manual management and the like) is explained.
Actuation schemes for energy systems. The main such schemes (daisy chain, split range, time division output) are treated, with the same approach just outlined for control structures.
Control-oriented modelling principles. The mathematical and modelling principles used in the course are introduced. As the focus is on system-level automation, this part aims at synthetically capturing the relevant dynamics with minimal complexity. To allow for physically interpretable parameters, first-principle modelling is privileged.
Electric systems. After presenting the above general ideas, these are monographically applied to the electric case, by discussing the management of an AC grid. The treatise includes primary and secondary power/frequency control, optimum generation allocation, and the basics of load flow. Care is taken to present the subjects above within an integrated view, and to evidence connections with neighbouring technologies. This means on the one hand relating the presented matter to detailed generator-level control, and on the other hand outlining the variety of forms taken by network-level optimisation when this involves multiple actors, conflicting objectives, bidding, and so forth.
Thermal systems. A second monographic part, with objectives similar to the one above and therefore structured in an analogous manner, is devoted to thermal systems, with specific reference to heat networks.
The concepts and methods learnt are put to work during practice hours with simple exercises, also in a view to preparing for the written test. Case studies of (slightly) higher complexity are addressed using open source control synthesis, analysis and simulation tools, made available to the students and introduced in the laboratory activity.

Prova scritta obbligatoria, senza prove in itinere
Progetto/i - Elaborato/i obbligatorio, di gruppo, senza revisioni periodiche, con presentazione/discussione finale
The assessment consists of two parts.
Project work (approximately 40% of the final mark)
The students will carry out a project using the methods and tools introduced in the course. They will deliver a brief written report, following a provided template, addressing control-oriented modelling and control synthesis for simple electric or heat networks, including simulation-based validation of the designed controllers.
The report will be evaluated for technical correctness, methodological soundness, and clarity of presentation — thus contributing to the assessment of Applying knowledge and understanding, Making judgements, and Communication.
Written test (approximately 60% of the final mark):
The written test covers the entire course content through both numerical exercises and open questions. It primarily assesses Knowledge and understanding and Applying knowledge and understanding.
To pass the exam the combined score must be at least 18/30, but a condition is that the written test alone scores at least 16/30.

Il corso offre una panoramica sul ruolo dell'automazione nel mondo dei trasporti, mostrando come le moderne tecnologie rendano possibile la gestione sicura ed efficiente dei sistemi di mobilità. Attraverso lezioni teoriche, discussioni in aula, incontri con aziende del settore e visite tecniche, gli studenti hanno l'opportunità di entrare in contatto con applicazioni reali e con le sfide dell'ingegneria nell'ambito dei trasporti.
Durante il percorso vengono analizzati i principali sistemi di trasporto: ferroviario, metropolitano, navale, aereo e stradale, evidenziandone le caratteristiche progettuali, costruttive e operative. Il corso fornisce le basi per comprendere il funzionamento dei sistemi di trasporto automatici, approfondendo gli aspetti legati alla gestione, alla sicurezza di esercizio e al rispetto della normativa di settore.
Ogni argomento è accompagnato da esempi applicativi e casi reali, con l'obiettivo di collegare la teoria alla pratica e di sviluppare una visione concreta delle soluzioni tecnologiche oggi impiegate nell'automazione dei trasporti.

Al termine del corso, gli studenti acquisiscono una visione d'insieme dell'automazione applicata al settore dei trasporti, comprendendone sia gli aspetti teorici sia quelli pratici. Le conoscenze acquisite consentono di comprendere le principali tecnologie impiegate nei diversi sistemi di trasporto, di confrontarsi con professionisti del settore durante gli incontri con le aziende e di interpretare documentazione tecnica specialistica. Inoltre, gli studenti sviluppano la capacità di identificare problemi e individuare possibili soluzioni nell'ambito dell'automazione dei trasporti (DdD1 e DdD2).
Dal punto di vista applicativo, il corso prevede lo sviluppo di progetti che permettono di affrontare problemi reali del settore. Attraverso queste attività, gli studenti imparano ad analizzare criticamente una problematica, a valutarne le possibili soluzioni utilizzando metodi consolidati e a scegliere quella più adatta al contesto specifico, definendone anche i criteri di implementazione.
Il lavoro progettuale rappresenta inoltre un'importante occasione per sviluppare competenze trasversali, come il lavoro di squadra, la comunicazione tecnica e la capacità di presentare in modo chiaro ed efficace i risultati ottenuti. Tali competenze vengono consolidate attraverso la presentazione finale del progetto davanti alla classe, durante la quale vengono valutate anche le capacità espositive e la padronanza degli argomenti trattati (DdD4).

Il corso affronta i principali ambiti dell'automazione applicata ai sistemi di trasporto, partendo dai concetti fondamentali fino ad arrivare alle applicazioni nei diversi mezzi di trasporto.
Argomento 1 – Passato, presente e futuro dei trasporti: a che punto siamo? Un'analisi dell'evoluzione dei sistemi di trasporto, dalle prime forme di automazione alle tecnologie più avanzate, per comprendere come sta cambiando il settore e quali sono le prospettive future.
Argomento 2 – Veicoli autonomi: realtà o futuro? Un approfondimento sullo stato dell'arte della guida autonoma, sulle tecnologie che la rendono possibile, sulle sfide ancora aperte e sulle applicazioni già presenti nel mondo reale.
Argomento 3 – Reti di comunicazione nei sistemi di trasporto elettrico: tecnologie di comunicazione che consentono lo scambio di informazioni tra dispositivi e sistemi di controllo.
Argomento 4 – Sistemi SCADA: architettura e funzionamento dei sistemi di supervisione, controllo e acquisizione dati utilizzati per il monitoraggio e la gestione degli impianti.
Argomento 5 – Sistemi automatici di trasporto a guida vincolata: analisi dei sistemi tradizionali e delle soluzioni innovative per il trasporto automatizzato, come metropolitane automatiche e people mover.
Argomento 6 – Sistemi di segnalamento ferroviario: principi e tecnologie impiegati per garantire la sicurezza e la regolazione della circolazione ferroviaria.
Argomento 7 – Veicolo elettrico: architettura, componenti principali e sistemi di controllo dei veicoli elettrici.
Argomento 8 – Automazione a bordo delle navi: sistemi di controllo e automazione impiegati nella gestione degli impianti navali.
Argomento 9 – Automazione a bordo degli aeromobili: principali sistemi automatici che supportano il pilotaggio, il controllo e la gestione delle funzioni di bordo.

Prova orale obbligatoria
Progetto/i - Elaborato/i obbligatorio, di gruppo, con revisioni periodiche, con presentazione/discussione finale
La valutazione del corso si basa:
Presentazione del progetto sviluppato durante il corso.
Approfondimenti relativi agli argomenti trattati durante le lezioni assegnati durante il corso e valutati singolarmente durante il corso.
Durante l'orale, per il progetto sviluppato in gruppo verranno valutati:
la capacità di esporre il progetto in modo chiaro;
la padronanza degli argomenti e la capacità di rispondere alle domande della commissione (DdD1 e DdD2);
la qualità e i contenuti della relazione di progetto (DdD4).

F. Foiadelli, M. Longo, Automazione dei Sistemi Elettrici di Trasporto: Il nuovo presente dei veicoli nel trasporto pubblico e privato, Editore: Edizioni Accademiche Italiane, Anno edizione: 2017, ISBN: 978-3639779011

Robotics has significantly evolved in the last years, moving from the traditional paradigms of the industrial robotics to new scenarios, where robots collaborate with humans, are aware of the environment where they operate, learn from experience, and even get human-like appearance. The purpose of this course is to expose the students to modern robotics, with special focus on collaborative applications, intelligent interaction, and humanoid robotics.

The expected learning outcomes of the course can be classified according to the Dublin descriptors.
Dd1 (Knowledge and understanding)
Students will understand
-the role and importance of collaborative robots and of the main safety standards relevant for robotics;
-the methods to have the robot learn from the experience;
-the kinematic and dynamic models of a humanoid robot;
-the principles to operate a humanoid robot.
Dd2 (Applying knowledge and understanding)
Students will learn how to:
-carry out a risk assessment for use of robots in collaborative applications;
-design learning systems to make the robot autonomous in unknown and complex environments;
-simulate a humanoid robot in a virtual environment;
-design meaningful tasks for humanoid robots.
Dd3 (Making judgement)
Students will also be able to decide which technique to adopt and how to apply it for the safe motion planning and control of robots in presence of humans.

1. Review of robot kinematics (*)
Rigid body kinematics.
Direct, inverse and differential kinematics of a robot arm.
Singularities.
2. Review of robot dynamics (*)
Dynamic model of a robot.
Inertia matrix.
3. Collaborative robotics
Motivations and use cases.
Collaborative applications.
4. Safety standards in collaborative robotics
Electromechanical drives with safety functionalities (STO, SOS, SLS, SLP).
Safety-rated monitored stop, hand guiding, speed and separation monitoring, power and force limiting.
Risk assessment: process, tools and methods. Examples.
5. Humanoid robots
Introduction to humanoid robots: motivations, classifications, market.
Complete dynamic model. Zero moment point. Simplified dynamic model: linear inverted pendulum.
Whole-body control.
Legged locomotion. Stability and gait generation.
Use cases in humanoid robotics.
6. Robot learning
Review on neural networks.
Learning from humans: Imitation learning, Dynamic Movement Primitives, DeepDMP
Learning on their own (Reinforcement Learning, SAC)
7. Emerging applications in robotics
Service robotics, robotics in construction, robotics in healthcare.
(*) These topics will be included only in the academic year 2026/27

Prova scritta obbligatoria, senza prove in itinere
The final assessment will be a written exam, consisting of both numerical exercises and theoretical questions.
In the written exam the student should be able to:
-apply the safety standards for collaborative robots to simple cases (Dd1, Dd2, Dd3);
-discuss the main methods for robot learning and solve simple related design problems (Dd1, Dd2);
-discuss the dynamic model of a humanoid robot (Dd1);
-discuss the locomotion of humanoid robots and solve simple related design problems (Dd1, Dd2).

Slides available in the WeBeep channel

The course is intended to provide students with the knowledge and tools necessary to design communication networks. In the first part of the course the different segments of networks (access, metro and core), with the role of optical-network technologies. Practical network design problems will be solved thorugh mathematical formulations (integer linear programming) and heuristic approaches, also with the help of a laboratory activity based on the open-source software Net2Plan. Various techniques to ensure network survivability against failures will be described. In the second part of the course, fundamental concepts of queuing theory will be introduced to solve capacity planning an network dimensioning problems (based on, e.g., Erlang and Engset formulas) considering different users' behavior.

Knowledge and Understanding (DD1)

• Students will learn how to:
  -- Use Integer Linear Programming to solve routing and capacity assignment problems
  -- Identify the right protection technique given the network characteristics
  -- Master fundamental tele-traffic formulas as Erlang B, C and Engset
  Applying Knowledge and Understanding (DD2)
  Given specific network characteristics, students will be able to:
  -- Model and implement network routing and capacity assignment problems
  -- apply fundamental tele-traffic formulas in realistic case-studies
  Making judgements (DD3)
  Students will be able to:
  -- understand the fundamental principles that govern the design of communication networks
  -- identify which protection strategy shall be appied based on service and network characteristics
  -- identify which tele-traffic fundamental formula shall be applied in practical capacity planning cases
  Communication (DD4)
• Students will learn how to present to a network engineer, in written and oral form, the actual design and operation problem that is facing in a communication network

1 – Introduction on Optical Networks
Optical networking principles and challenges: telecom network overview, business models, traffic engineering vs network engineering vs network design. Enabling Technologies: optical fiber, optical transmitters, optical receivers, optical amplifiers, switching elements, Wavelength Division Multiplexing (WDM).
2 – Exact and Heuristic (Optical Core) Network design methodologies
Network design based on mathematical modelling: flow formulation, route formulation. Modelling of network protection: dedicated protection, shared protection. Network design by heuristic approaches: greedy, local search. Lab activity with Net2Plan: a free and open-source Java tool for the design of communication networks
3 – Protection techniques
Network survivability: objectives and protection techniques. Single-layer and multi-layer protection techniques: protection at IP layer, protection at physical layer. Protection techniques in SONET/SDH: line and section protection, point-to-point and ring structures, dedicated and shared protection. Protection in the optical layer: solutions for ring networks and for mesh network, dedicated-vs-shared protection, ring cover and p-cycle techniques.
5 – Queueing theory primer
Markov, birth death and Poisson process.
6 – Capacity dimensioning in communication networks
Introduction on traffic modeling: single and multiple source model. Analysis of multiple-server systems with assumption BCC, BCH, BCD. Evaluation of congestion and statistics of carried/lost traffic (Erlang B and C, Engset). Capacity dimensioning with Wilkinson, Fredericks and Lindberger approaches.

Prova scritta obbligatoria, senza prove in itinere
Prova orale facoltativa (a scelta dello studente)
Progetto/i - Elaborato/i facoltativo (a scelta dello studente), di gruppo, con revisioni periodiche, con presentazione/discussione finale
The verification of knowledge for the course content consists in a test at the end of the course that comprises a written test and, if required, an oral examination on the subjects covered in the course (DD1). Projects, to be defined with the professor, can be used to substitute the oral examination.
The written exam (DD2, DD3) is based on numerical exercises (up to 5) and/or open question (1 or 2) on the topics such as protection, network design thorugh Integer Linear Programming and capacity dimensioning using queueing theory formulas. the written exam is used to assess the capability of the students to manage theoretical and technological tools to design communication networks. The laboratory activity using the software Net2Plan and NetworkX Python Libraries can be used to complement the final written score.
The oral exam (DD4) will be used by the instructor to verify student' understading of the fundamental concepts such as enabling technologies for networking, queuing theory behind formulas for capcity dimensioning, protection at different network layers.
In case of negative evaluation the student is admitted to the following tests of the academic year.

Materiale corso https://webeep.polimi.it/course/view.php?id=21359

Il corso ha lo scopo di illustrare metodi e algoritmi per l'analisi di sistemi complessi, vale a dire sistemi composti da un numero elevato di unità tra loro interagenti. Rientrano in questa categoria, a titolo di esempio, le reti sociali e quelle infrastrutturali per la distribuzione di energia, materia o informazione, le reti di transazioni economico-finanziarie e quelle di interazione biologica (p.e. proteine e processi metabolici), gli ecosistemi e le flotte di agenti (naturali o artificiali) coordinati. Il corso illustra dapprima un insieme di strumenti per l'analisi e la caratterizzazione della struttura delle reti, per poi considerare i fenomeni emergenti dall'interazione di sistemi dinamici attraverso la rete. Ogni argomento sarà trattato sia dal punto di vista teorico che attraverso la discussione di esempi applicativi.

Descrittori di Dublino
Risultati di apprendimento attesi
Conoscenza e comprensione
A seguito del superamento dell’esame lo studente conosce:
I principali problemi, i metodi di analisi, e gli algoritmi fondamentali per l’analisi delle proprietà topologiche di una rete complessa.
I principali problemi, i metodi di analisi, e gli algoritmi fondamentali per l’analisi delle proprietà dinamiche di un insieme di sistemi tra loro interagenti (sistemi complessi).
Capacità di applicare conoscenza e comprensione
Attraverso la teoria, le esercitazioni e i casi applicativi analizzati, e a seguito del superamento dell’esame, lo studente acquisisce la capacità di applicare i metodi e gli algoritmi appresi a casi reali, per quanto di complessità limitata.
Abilità comunicative
A seguito del superamento dell’esame, in particolare della prova integrativa, lo studente è in grado di comunicare in modo chiaro i contenuti di una ricerca scientifica, evidenziando i metodi che sono stati adottati e i risultati ottenuti.
Capacità di apprendimento
A seguito del superamento dell’esame, lo studente acquisisce la capacità di apprendere in maniera autonoma argomenti legati alle tematiche generali affrontate durante il corso.

Introduzione allo studio della complessità nei sistemi e nelle reti
Reti complesse:
Distanze, diametro, clustering, distribuzione di grado
Modelli di rete: random, scale-free, small-world
Misure di centralità: closeness, betweenness, PageRank
Analisi di comunità e core-periphery
Robustezza infrastrutturale: resilienza e tolleranza ai guasti
Sistemi complessi:
Robustezza infrastrutturale: propagazione di guasti
Processi di diffusione in rete
Reti di sistemi dinamici
Dinamiche collettive: consenso e sincronizzazione
Per ulteriori informazioni si rimanda alle pagine personali del docente ( https://piccardi.faculty.polimi.it/csr.html ), dove sono anche disponibili le slide usate a lezione e vari articoli di approfondimento.

Prova scritta obbligatoria, senza prove in itinere
Prova orale condizionata (a scelta del docente)
Progetto/i - Elaborato/i facoltativo (a scelta dello studente), individuale, senza revisioni periodiche, con presentazione/discussione finale
L'esame consiste in una prova teorica scritta (esercizi + domande teoriche a risposta 'aperta' e 'chiusa') alla quale viene assegnata una valutazione massima di 32 punti. Lo studente non ha la facoltà di richiedere una prova orale, che è invece a discrezione del docente qualora, in casi particolari, voglia verificare l'effettiva preparazione dello studente.
La prova integrativa (progetto) è facoltativa e riservata agli studenti frequentanti (il docente specificherà le modalità di verifica della frequenza): su richiesta, allo studente viene assegnato un articolo scientifico, analizzato il quale dovrà preparare una presentazione orale supportata da slide, della durata di 10/15 minuti, che esporrà in aula. La prova integrativa ha una valutazione massima di 1.5 punti, i quali si sommano al risultato della prova teorica dando luogo al voto finale.
La seguente tabella mostra in dettaglio gli elementi che saranno considerati nella valutazione:
Modalità di verifica
Descrizione
Risultato di apprendimento perseguito (descrittore di Dublino)
· Prova teorica (scritta)
· Domande di carattere teorico a risposta aperta e chiusa sugli argomenti del corso
· Risoluzione di esercizi numerici
· 1,2,4,5
· 1,2
· Prova integrativa (orale)
· Presentazione del contenuto di articolo scientifico assegnato e discussione
· 1,2,4,5

Albert-László Barabási, Network Science, Editore: Cambridge University Press, Anno edizione: 2016, ISBN: 1107076269
M.E.J. Newman, Networks: an Introduction, Editore: Oxford University Press, Anno edizione: 2010, ISBN: 978-0-19-920665-0
A. Pikovsky, M. Rosenblum, J. Kurths, Synchronization - A Universal Concept in Nonlinear Sciences, Editore: Cambridge University Press, Anno edizione: 2001, ISBN: 0-521-59285-2

Modern large-scale datacenters require the seamless integration of different components - applications, computation nodes, storage devices, and networks - into one computing infrastructure. The course covers the basics of current datacenters architectures, ranging from the analysis of the single components to the global infrastructure. The focus is on the foundations required to understand the design of computing infrastructures that are scalable, available, secure and flexible at the same time. Virtualization, container-based technology, cloud computing and storage systems are analyzed in depth to show how they can be used to support challenging tasks such as machine-learning applications and high-performance computing.

Knowledge and understanding (DD1):

• The student will acquire knowledge about the Computing Infrastructure field. In particular, the students will learn the essential elements that are part of a scalable data center and the basic concepts of reliability, performance evaluations, virtualization, and cloud computing.
  Applying knowledge and understanding (DD2):
• The student will be able to understand the design choices done within a large-scale computing infrastructure.
• The student will be able to design both computational and storage components of a data center, to satisfy specific service-level requirements
• The student will be able to apply performance and reliability analysis to the data center and to other complex systems, even outside the computer engineering domain
  Making judgements (DD3):
• The student will be able to explore alternative solutions within a data center, in terms of computing architectures and service models.
• The student will be able to analyze and discuss the trade-offs between reliability, performance, and costs of different hardware and software technologies within a data center.
  Communication skills (DD4):
• The student will be able to use the data center terminology appropriately.
• The student will be able to illustrate the characteristics and functionality of a data center infrastructure and related service models.
  Learning skills (DD5):
• The student will develop learning skills necessary to autonomously extend performance and reliability analysis to other types of components in a data center, and to autonomously understand novel technologies within computing infrastructures together with their impact.

Hardware infrastructure of datacenters:

• Basic components, Rack structure, Cooling
• Hard Disk Drive and Solid State Disks
• RAID architectures
• Hardware Accelerators
  Software infrastructure of datacenters:
• Virtualization: basic concepts, technologies, hypervisors and containers
• Computing Architecture: Cloud, Edge and Fog Computing
• Infrastructure-, platform- and software-as-a-service
  Methods:
• Scalability and performance of datacenters: definitions, fundamental laws, queuing network theory basics
• Reliability and availability of datacenters: definitions, fundamental laws, reliability block diagrams
  Exercises to apply the knowledge in simple examples or to more complicated scenarios will be used especially considering the parts related to reliability, storage system design, and performance analysis.

Prova scritta obbligatoria, senza prove in itinere
Prova orale condizionata (a scelta del docente)
The evaluation consists of a written exam on the topics covered in the course. The exam will include both theoretical questions and numerical problems.
More in detail the written exam includes:

• Close-ended questions on course topics to verify the capacity to recognize the basic concept behind the design of a large-scale computing infrastructure; (DD1, DD2)
• Open-ended theoretical questions on course topics to verify the capacity to describe the key concepts related to the design of a large-scale computing infrastructure; (DD1, DD2, DD3, DD4, DD5)
• Numerical problem resolutions regarding analysis and design of data center and data center components, virtualization, storage systems, dependability, and performance modeling. (DD1, DD2, DD3)
  An oral test could be requested by the lecturer to deepen specific questions of the written test and further verify the capacity of the student to describe the key concepts related to the design of a large-scale computing infrastructure and develop links between the various topics of the course (DD1, DD2, DD3, DD4, DD5)

Course Web Site webeep.polimi.it
Luiz André Barroso and Urs Hölzle, The Datacenter as a Computer: An Introduction to the Design of Warehouse-Scale Machines
Edward D. Lazowska, John Zahorjan,G. Scott Graham, Kenneth C. Sevcik, Quantitative System Performance: Computer System Analysis Using Queueing Network Models
Note: (Open Access Book) (chapters 1-4)
James Smith and Ravi Nair, "Virtual Machines" Versatile Platforms for Systems and Processes
Caesar Wu and Rajkumar Buyya, Cloud Data Centers and Cost Modeling

Il corso si propone di introdurre lo studente agli aspetti fondamentali della modellistica, della pianificazione e del controllo di robot mobili, robot mobili autonomi e manipolatori mobili.
Verranno illustrate le principali problematiche della robotica mobile e della manipolazione mobile per ambienti indoor, outdoor, e per robot fuori strada; le tecniche classiche e moderne per affrontare problemi di pianificazione e controllo.
Il corso si concluderà con la presentazione di un caso di studio per permettere allo studente di apprezzare l'applicazione delle metodologie di modellistica, pianificazione e controllo ad un problema realistico.

Il corso si propone di introdurre lo studente agli aspetti fondamentali della modellistica, della pianificazione e del controllo di robot mobili, robot mobili autonomi e manipolatori mobili.
Verranno illustrate le principali problematiche della robotica mobile e della manipolazione mobile per ambienti indoor, outdoor, e per robot fuori strada; le tecniche classiche e moderne per affrontare problemi di pianificazione e controllo.
Il corso si concluderà con la presentazione di un caso di studio per permettere allo studente di apprezzare l'applicazione delle metodologie di modellistica, pianificazione e controllo ad un problema realistico.

The goal of this course is to present current and advanced methodologies for the control of robotic manipulators. The course covers selected topics ranging from kinematic and dynamic modelling of an industrial robot, to advanced motion planning and control, to control of the interaction of the robot with the environment based on force and vision feedback. The goal of the course is fully aligned with the overall goals of the Automation and Control Engineering Program, while being an excellent complement for students enrolled in other Programs (Biomedical Engineering, Computer Science and Engineering, Electronics Engineering, Engineering Physics, and others).
A mix of theoretical and industrially relevant topics characterizes the course, where extensive use of software for simulation and offline programming of robots will be made.

The expected learning outcomes of the course can be classified according to the Dublin descriptors.
Dd1 (Knowledge and understanding)
Students will understand:

• the role of industrial robots in the factory, why and where they should be used in the production systems;
• the dynamic model of a robot;
• the advanced tools for planning and control of the motion of the robots;
• the interaction of the robot with the environment;
• the role and importance of collaborative robots.
  Dd2 (Applying knowledge and understanding)
  Students will learn how to:
  -use mathematics to describe the motion of a robot, in terms of both kinematics and dynamics;
  -plan a suitable motion for the robot both in free environment and in presence of obstacles;
  -tune an industrial motion control system and understand the rationale and potentialities of advanced nonlinear model based control strategies;
  -master the control of the interaction of the robot with the environment, either with force or with vision sensors;
  -master software programs to simulate and to offline program the robots.
  Dd3 (Making judgement)
  Students will be able to decide which modelling, planning and tracking technique to adopt and how to apply them for the motion planning and control of industrial robotis

1. Introduction
Industrial robots: basic concepts and examples.
Market of industrial robotics.
Trends in industrial robotics.
2. Advanced robot kinematics
Review of direct, inverse and differential kinematics.
Kinematic calibration.
Kinematics of redundant manipulators: methods for redundancy resolution.
3. Robot dynamics
Dynamic models of robot manipulators.
Euler-Lagrange and Newton-Euler formulations: main properties.
Identification of dynamic parameters.
Direct and inverse dynamics.
4. Advanced motion planning
Review of motion generation in joint space and in operational space.
Path planning in joint space and in the operational space.
Path planning with obstacle avoidance.
Kinematic and dynamic scaling of trajectories.
Interpolation of points (splines).
5. Control of robot manipulators
Review of independent joint control methods.
Centralized model-based controllers: computed torque feedforward control, PD control with gravity compensation, inverse dynamics control, robust and adaptive control.
Operational space control.
6. Interaction with the environment
Force sensors.
Impedance and admittance control.
Hybrid position/force control.
7. Control with vision sensors
Image processing.
Camera calibration.
Image-based and position-based control systems.
Interaction matrix and image Jacobian.
8. Collaborative robotics (notes)
Human-robot interaction.
Collaborative applications.
Some of the practice sessions will make use of computer simulation tools and of commercial tools for robot offline programming.

Prova scritta obbligatoria, senza prove in itinere
The final assessment will be a written exam, consisting of both numerical exercises and theoretical questions.
In the written exam the student should be able to:
-compute the dynamic model of simple two degrees of freedom robots (Dd1, Dd2);
-discuss the properties of a kinematically redundant robot (Dd1);
-compute or rescale a trajectory in joint space or in the operational space (Dd1, Dd2, Dd3);
-discuss the main properties of centralized model-based controllers and solve simple numerical related design problems (Dd1, Dd2. Dd3);
-discuss the main properties of the control with force and vision sensors and solve simple numerical related design problems (Dd1, Dd2).

Bruno Siciliano, Luigi Villani, Giuseppe Oriolo, Alessandro De Luca, Foundations of Robotics, Editore: Springer, Anno edizione: 2025, ISBN: 978-3-031-85522-1 https://doi.org/10.1007/978-3-031-85523-8
B. Siciliano, L. Sciavicco, L. Villani, G. Oriolo, Robotica: modellistica, pianificazione e controllo, 3a Ed., Editore: McGraw-Hill Italia, Anno edizione: 2008, ISBN: 9788838663222 http://www.catalogo.mcgraw-hill.it/catLibro.asp?item_id=2317
G. Magnani, G. Ferretti, P. Rocco, Tecnologie dei sistemi di controllo, 2a Ed., Editore: McGraw-Hill Italia, Anno edizione: 2007, ISBN: 9788838672750 http://www.catalogo.mcgraw-hill.it/catLibro.asp?item_id=2141

The Critical Thinking course aims to provide, in an innovative manner, the methodological tools necessary to reason and argue soundly under conditions of risk and uncertainty in science and technology. The tools used will be those belonging to logic, philosophy, argumentation, and the psychology of thought, as applied to uncertain reasoning. The course content will be presented and discussed interactively with students. The course is intended to give students the opportunity to reflect critically on the cultural, social, and ethical impacts of science and technology

( Knowledge and understanding )
Students will:

• be acquainted with different forms of scientific reasoning
• acquire methodological skills in order to deal with risk and uncertainty
  ( Applying knowledge and understanding )
  Students will:
• foster their ability to solve problems and cope with uncertainty
• apply critical thinking to complex problems
• integrate their technical knowledge with humanistic, cognitive and ethical perspectives on science and technology
  ( Making judgements )
  Students will be able to:
• use methods and imagine scenarios in order to deal with (severe) uncertainty
• enhance creativity in thinking and reasoning
  ( Communication )
  Students will learn how to
• improve their skills in a writing an academic paper
• make academic presentations
• evaluate different forms of (risk) communication
  ( Lifelong learning skills )
  Students will
• be ready to evaluate and take decisions under conditions of uncertainty
• be better prepared to their future professional life in a socially responsible way
• be able to propose innovation solutions to complex and uncertain problems.

The course will cover different topics both from a theoretical and a more practical point of view.
The list of topics includes:

• scientific reasoning: deduction, induction, and abduction
• elements of logic and argumentation
• probabilistic reasoning: biases and paradoxes
• risk theory
• risk perception
• risk communication
• risk and moral argumentation
• decision making under severe uncertainty
• technological innovation and uncertainty
• scenario planning
• sustainability and scientific values

Progetto/i - Elaborato/i obbligatorio, individuale, senza revisioni periodiche, con presentazione/discussione finale
An individual final essay
Students are required to develop an argumentative thesis in an individual essay in which the specific claims are justified by the use of critical thinking and the relevant literature.
Students are required to read and carefully study the suggested bibliography and to apply it to case-studies.
Collective presentations
Students are encouraged to give collective presentations in order to reduce the length of the individual final essay (this option is available only for some study programs). All detailed information and the pdfs of the scientific papers that we will be used will be made available.
The individual final essay will be evaluated considering the following criteria:
1) originality of the main thesis
2) knowledge of the scientific and technological background
3) argumentative skills
4) ability to integrate technical knowledge with critical reflection
5) compliance with academic standards

Damiano Canale, Roberto Ciuni, Aldo Frigerio, Giovanni Tuzet, Critical Thinking. An Introduction, Editore: Bocconi University Press - Egea, Anno edizione: 2021, ISBN: 9788899902858
Daniele Chiffi, Stefano Moroni, Uncertainty, Innovation and the City, Editore: Edward Elgar, Anno edizione: 2025, ISBN: 9781035342662 https://www.e-elgar.com/shop/gbp/uncertainty-innovation-and-the-city-9781035342662.html?srsltid=AfmBOoooZkaZdrxskmSY2ckahsDBQ2HOVkhHF7S-AU-24J5_HcpovGK-
Mattia Andreoletti, Daniele Chiffi, Philosophy of Risk, Editore: Cambridge University Press, Anno edizione: 2027
Note: If not yet published, an advanced draft of the book will be made available to students.

Data-driven and algorithmic technologies increasingly support or automate decisions in engineering, business, science, public policy and everyday life. Their outcomes, however, are not determined by technical performance alone: they also depend on how data are generated, selected and represented, on the objectives and constraints embedded in algorithms, and on the social contexts in which technological systems are designed and deployed.
The course provides students with an interdisciplinary framework for understanding, analysing and addressing fairness-related issues in data-driven technologies. It combines a sociological perspective on data, datafication and algorithmic processes with technical methods for identifying bias, evaluating different notions of fairness and designing fairness-aware technological and decision-making solutions.
The course is intended for students from different engineering backgrounds. It aims to show that data and algorithmic fairness is not a specialised topic limited to diversity and inclusion, but a central engineering challenge concerning the quality, reliability, legitimacy and societal impact of technological systems.
The sociological module introduces students to the relationship between data and society, considering the production and use of data as both a technological activity and a socio-political process. The technical module focuses on the mechanisms through which unfair outcomes may arise in data, algorithms and decision-making processes, and on methods and frameworks for evaluating and mitigating them.
Through lectures, discussions, case studies, exercises and a group project, students will learn to connect sociological interpretation, technical analysis and engineering design.

Upon successful completion of the course, students will be able to:
Explain and critically interpret how data and algorithmic systems are shaped by social, cultural, political and technological processes, and how their design and deployment may reproduce or transform existing inequalities.
Dublin Descriptor 1 — Knowledge and understanding
Analyse data-driven and algorithmic systems from a fairness perspective, identifying potential sources of bias across the different stages of the technological lifecycle and applying suitable concepts, criteria and methods to assess their outcomes.
Dublin Descriptor 2 — Applying knowledge and understanding
Formulate and justify fairness-aware design or policy choices, critically discussing alternative solutions, conflicting objectives, limitations and trade-offs, and clearly communicating the results of the analysis to audiences with different disciplinary backgrounds.
Dublin Descriptors 3 and 4 — Making judgements and communication skills
Class activities, independent study and project work will also support students in integrating technical and sociological perspectives, using feedback constructively and autonomously extending the course concepts to new application domains.
Dublin Descriptor 5 — Learning skills

The course is organised into two closely integrated modules.
Module A — Data and society: sociological perspectives
2 CFU
The first module introduces a sociological perspective on data and algorithmic processes. Data are considered not only as technical objects, but also as the result of social choices, classifications, institutional practices and power relations.
Topics include:
introduction to sociology and to the relationship between society and technology;
data, society and processes of datafication;
the social construction of data, categories and classifications;
identity, culture and technological systems;
the relationship between technology, power and inequality;
the role of gender, race and ethnicity, age, disability and socioeconomic conditions in the production and use of data;
historical and contemporary case studies concerning data-driven technologies;
data feminism and design justice;
epistemic injustice and its relevance to data production, technological design and decision-making;
the social, cultural and political implications of algorithmic systems in scientific, business, policy, healthcare and everyday-life contexts.
The module combines lectures with in-class discussions, exercises and case-study activities. This reflects the approach proposed for the sociological module, in which data production and application are treated as both technological and socio-political phenomena.

Prova scritta obbligatoria, senza prove in itinere
Valutazione continua obbligatoria
The course adopts an interdisciplinary and student-centred approach. Teaching activities combine:
lectures introducing sociological concepts, technical methods and analytical frameworks;
guided discussions on scientific literature, technological applications and controversial cases;
individual and small-group exercises;
analysis of real or realistic case studies;
activities requiring students to compare alternative definitions and approaches to fairness;
supervised development of a group project;
final presentation and discussion of project results.
The group project requires students to select or analyse a data-driven or algorithmic application and examine it from both a sociological and a technical fairness perspective. Depending on the selected case, students may analyse data generation, model assumptions, performance across population groups, decision rules, deployment conditions, affected stakeholders and possible mitigation strategies.
The project is intended to develop the ability to integrate perspectives from different disciplines and to formulate technically informed and socially aware design recommendations.
Indicative teaching mix
Teaching activity
Hours
Didattica trasmissiva/frontale
28
Didattica interattiva/partecipativa
12
Didattica laboratoriale
4
Didattica progettuale
6
Didattica valutativa
0
Total assisted teaching
50
Independent study and project work
75
Assisted teaching hours per CFU
10
This classification is consistent with the current distinction between transmissive teaching, interactive activities, laboratory work and project-based teaching. In particular, discussions, guided exercises and case-study activities fall within interactive teaching, while the supervised development and revision of the group assignment are appropriately classified as project-based teaching.

In control and filter design problems, one deals with systems that often are affected by uncertainty so that their behavior is not deterministically known and, possibly, it is also time varying. In this situation, data-driven control design schemes can be adopted, where design is not based on a fixed model of the system, but rather modeling is progressively updated based on data obtained from the actually observed system response and the better control action to achieve the desired goal is learned through experience. This course aims at illustrating some of the main techniques in data-driven control and at demonstrating their effectiveness in counteracting the presence of uncertainty; correspondingly, advance identification and learning techniques to acquire knowledge on the system dynamics from measured data will be introduced and discussed. Computer simulations carried over during classes will be used to consolidate concepts.

Lectures and exercise sessions (including computer laboratory classes) will enable students to:

• understand how the wealth of information contained in data can be processed and exploited for control and system design purposes ( DdD1 - capability of knowledge and understanding );
• understand the assumptions and functioning principles underlying some common data-driven control design techniques ( DdD1 - capability of knowledge and understanding );
• recognize the situations where the design of control systems can profitably take advantage of learning from data and from the observation of the system behavior ( DdD1 - capability of knowledge and understanding, DdD2 - capability of applying knowledge and understanding );
• determine which approaches are most appropriate for a given application and predict the impact of algorithmic choices and data preprocessing on the robustness and effectiveness of the resulting control scheme ( DdD2 - capability of applying knowledge and understanding, DdD3 - capability of making judgements );
• implement, at an algorithmic level, some of the methods and approaches presented in the course ( DdD2 - capability of applying knowledge and understanding, DdD3 - capability of making judgements ). This last objective will be pursued in particular through the computer laboratory classes.
  Following successful completion of the examination, students will be able to communicate their knowledge clearly and convincingly ( DdD4 ).

Uncertainty, data, adaptation and robustness
Uncertainty, robustness, and adaptation in control. Principles of data-driven control. On-line and off-line schemes. Direct and indirect schemes.
Indirect data-driven control schemes
The self-tuning paradigm. Certainty equivalence principle. Identification resumed: least-squares identification, recursive identification, forgetting factor. Main properties of the self-tuning scheme: convergence of the model estimate, stability and optimality. Examples.
Direct data-driven control schemes
Principles of direct data-driven control. Taxonomy of existing approaches (IFT, VRFT,…). The VRFT (Virtual Reference Feedback Tuning) approach: principles. Frequency interpretation of PEM identification methods. Optimality and sub-optimality of the VRFT controller, optimal data pre-filtering. VRFT in presence of additive disturbances. Instrumental Variable identification. The VRFT toolbox of MATLAB.
Data-driven approaches to optimal control
Markov Chain Decision Processes (MCDP). Optimal control of a MCDP. Discounted costs. Principles of dynamic programming: the Bellman equation. Data-driven approaches to dynamic programming. Reinforcement learning. The Q-learning algorithm. Exploitation vs. exploration issues.

Prova scritta obbligatoria, senza prove in itinere
The final exam is a written exam, constituted by four questions. The duration is 2h.
More specifically:
The first three questions are theoretical open questions on topics covered throughout the course programme. These questions are intended to assess the level of knowledge and understanding achieved by the student. One of these questions may consist of a numerical exercise designed to assess the student’s ability to apply the acquired knowledge to solve problems. These questions are aimed at assessing the student’s knowledge and understanding (DdD1) and ability to apply knowledge and understanding (DdD2).
In the fourth question, the student is required to address a situation that, although related to the topics covered in the course, has not been dealt with directly in class. This question is aimed at assessing the student’s ability to establish connections among the various topics covered in the course and to apply the acquired knowledge to new and innovative contexts (DdD3).
The examination will also assess the student’s ability to communicate their knowledge clearly and convincingly (DdD4).

Notes and papers provided by the lecturer
Note: Additional information available on the course web-site http://corsi.dei.polimi.it/IMAD/SAA/
S. Bittanti, Identificazione dei modelli e sistemi adattativi, Editore: Pitagora Editrice
S. Bittanti, Teoria della predizione e del filtraggio, Editore: Pitagora Editrice
S. Bittanti (ed.), Simulazione, identificazione, controllo: il caso di uno scambiatore di calore, Editore: Pitagora Editrice

The course fits into the overall program curriculum pursuing some of the defined general learning goals. In particular, the course contributes to the development of the following capabilities:
Understand context, functions, processes in a business and industrial environment and the impact of those factors on business performance
Identify trends, technologies and key methodologies in a specific domain (specialization streams)
Design solutions applying a scientific and engineering approach (Analysis, Learning, Reasoning, and Modeling capability deriving from a solid and rigorous multidisciplinary background) to face problems and opportunities in a business and industrial environment

The main expected learning outcomes for each of the following capabilities are:
1. Understand: the students will learn how the automation and digital technolgoies (i.e. Industry 4.0) are effecting the future manufacturing systems, both in the process industry as well as in the discrete manufacturing systems;
2. Identify: the students will learn how to select (i.e. identify) the best technology and the best application scenario;
3. Design: designing a manufacturing systems is a complex process which requires to deal with the hard, soft and organisational dimensions of the design of the system. Quantitative and qualitative techniques will be explained. The impact evaluation will also be assessed in order to give students the know-how on how to understand the impact of their decisions.

Part I – Advanced and Sustainable Manufacturing Strategies (9 hours)
1.1. Sustainable development: social, environmental, and economic megatrends — 3 hours
Examines the global trends reshaping industry and society, and how sustainability considerations are embedded into manufacturing strategy.
1.2. The role of manufacturing in the regional, national, and global economy — 3 hours
Analyzes how manufacturing drives economic growth, employment, and competitiveness across different geographic scales.
1.3. Manufacturing as a key enabler of Europe's societal challenges — 3 hours
Explores how manufacturing contributes to addressing major European priorities such as climate action, resource efficiency, and industrial resilience.
Part II – Digital Manufacturing in the Process Industry (9 hours)
2.1. Process control — 4 hours
Introduces the principles and tools used to monitor and regulate continuous production processes.
2.2. Control architecture typologies: monitoring, direct digital control, and supervisory control — 5 hours
Compares different levels of automation and control system architecture used in process industries.
Part III – Digital Manufacturing in the Discrete Manufacturing Industry (32 hours)
3.1. Design and management of Advanced Manufacturing Systems (AMS) — 6 hours
Covers the design principles and operational management of flexible, technology-driven production systems.
3.2. Real-time control systems: loading and dispatching approaches — 5 hours
Examines methods for scheduling and coordinating production activities in real time on the shop floor.
3.3. Introduction to industrial robots and humanoid applications in manufacturing — 5 hours
Provides an overview of robotic technologies, including humanoid robots, and their growing role in production environments.
3.4. Artificial Intelligence (AI) in manufacturing — 6 hours
Explores how AI techniques are applied to optimize processes, predict outcomes, and support decision-making in manufacturing.
3.5. Human-machine interaction — 4 hours
Studies how humans and machines collaborate effectively and safely within modern manufacturing systems.
3.6. Business case analysis (class work, seminars, etc.) — 6 hours
Applies course concepts to real-world industry cases through hands-on classroom activities and seminar discussions.

Prova orale obbligatoria
The final exam will be conducted orally. Each student will be asked a series of questions covering the topics addressed throughout the course, and will be expected to answer clearly and demonstrate a solid understanding of the material, including the ability to connect concepts across different parts of the syllabus. The exam typically lasts between 30 and 45 minutes per student, depending on the depth of the answers given. Students should be prepared to discuss theoretical concepts, provide examples, and, where relevant, work through problems or reasoning aloud. The final grade will reflect not only the accuracy of the answers but also the clarity of exposition and the ability to reason critically about the subject matter.

L'utilizzo di modelli in ambito industriale è ad oggi sempre più diffuso, spesso sotto forma di un Digital-Twin che simuli l'intero ciclo vita di un sistema nel suo ambiente operativo. Tali modelli, simulando l'evoluzione di variabili continue nel tempo e l'occorrenza di eventi discreti, possono essere utilizzati per l'ottimizzazione della gestione del sistema e del processo decisionale, attraverso opportune analisi del rapporto tra costi e benefici.
A seguito di un breve richiamo degli aspetti fondamentali relativi alla gestione del ciclo vita, alle strategie di manutenzione e ai meccanismi di degrado e guasto tipici di un sistema industriale, lo studente imparerà a simulare un ambiente operativo industriale mediante diverse strategie di modellazione di fenomeni continui nel tempo, eventi discreti, e logiche decisionali. Ciascun argomento sarà affiancato da esercitazioni pratiche su casi realistici.
Il corso prevede infine un'esperienza di laboratorio informatico in cui gli studenti, con l'ausilio di un tutor, impareranno a modellare e simulare lo scenario operativo di un vero sistema industriale.

Il corso si concentra sui gemelli digitali dei sistemi di produzione, che sono repliche digitali di sistemi fisici utilizzati per supportare la presa di decisioni durante le fasi di progettazione, produzione e operazione degli impianti industriali. L'aspetto centrale del corso è la modellazione e le tecniche di analisi della simulazione ad eventi discreti che costituiscono il nucleo della tecnologia dei gemelli digitali.

"L'obiettivo del corso è quello di presentare alcuni dei principali concetti e metodi di Ottimizzazione Discreta che consentono di affrontare un'ampia varietà di problemi decisionali che sorgono in scienza, ingegneria e management. L'attenzione si concentra sulla programmazione lineare mista intera (PLMI), cioè sui problemi di ottimizzazione con una funzione obiettivo lineare e vincoli lineari in cui alcune (o tutte) le variabili sono limitate a prendere valori discreti (interi). Vengono trattati anche aspetti modellistici e relativi alle applicazioni.
Argomenti: Fondamenti di analisi convessa. Problemi di PLMI e tecniche di modellazione. Formulazioni alternative, forti e ideali. Problemi di PLI ""facili"". Rilassamenti lagrangiani, surrogati e combinatori. Euristiche (greedy e ricerca locale). Strategie di branching per il metodo Branch-and-Bound. Metodi dei piani di taglio: tagli per problemi di PLI generici, disuguaglianze valide forti per problemi specifici di PLI, separazione e rafforzamento. Metodo Branch-and-Cut. Metodo di generazione delle colonne.
Durante le sessioni di laboratorio informatico, gli studenti impareranno a risolvere problemi di PLMI con il linguaggio di modellazione AMPL e un risolutore stato dell'arte. "

Course objectives:

• to provide the theoretical knowledge and the numerical approaches necessary to describe and critically analyze the dynamic behavior of conventional facilities in the field of chemical engineering;
• to provide the theoretical foundation of control theory, covering from conventional feedback control strategies already introduced in previous fundamental courses to feedforward, model guided, model predictive control and other advanced techniques applied both to single facilities and to networks of units (i.e. simplified or real chemical plants)
• to apply the theoretical notions to the selection of the most suitable control strategies and to the design of feedback, feedforward and model guided controllers by means of numerical simulations

Lectures and computational laboratories sessions will allow students to:

• derive conservation equations to model the dynamics of facilities and units of interest in chemical processes (tank, series of tanks, heaters, reactors, distillation columns, flash separators, mixers,...)
• analyze the features and criticisms of a dynamic system in relation to stable and unstable behaviors
• assess the relation between degrees of freedom and control systems and is able to conceive suitable strategies to react to external disturbances and set point changes
• discern between advantages and disadvantages of different possible control strategies
• design effective controllers for a given case study
  Specifically, computational laboratories and the team work to produce the report will allow students to:
• implement numerical algorithm to describe the open loop, closed loop, model based control behavior of relevant systems in commercial software for the solution of dynamic systems (e.g. Matlab, Simulink)
• discuss critically and with an adequate technical language process dynamics and process control aspects
• analyze and improve numerical algorithms with respect to solutions proposed in class
• improve communication skills and team group capabilities

Development of mathematical models for chemical engineering applications:
Conservation equations and process variables for ideal reactors and unit operations
Kinetics, thermodynamics and transport equations
Input/output model
Degrees of freedom
Feedback control systems design
Dynamics of feedback controlled processes: effects of control actions
Stable/Unstable systems
Design of feedback controllers
Feedback controllers design synthesis
Advanced control systems concepts and design
Multiple loop control
Selective control
Feedforward control
Feedforward plus feedback
Adaptive and inferential control
Control of distillation columns
Plant control: control systems for multivariable processes (MIMO)
Control of simplified process layouts
Loops interaction and decoupling
Together with lectures on theoretical aspects of process dynamics and control, computer laboratories will target the implementation and solution of mathematical models using Matlab, simulink and other tools (process identification, model predictive control).

Prova scritta obbligatoria, senza prove in itinere
Progetto/i - Elaborato/i obbligatorio, di gruppo, senza revisioni periodiche, senza presentazione/discussione finale
The students are required to produce a detailed, original and creative group report covering all computational laboratory sessions, critically bridging between theory classes and the many aspects addressed during practicals. Submission of this report is mandatory to access the exam. Reports are evaluated on a 0 to (0-5/30) scale that will complement the official exam evaluation.
The official exam is a written test covering theory notions and concepts from lectures, also including short exercise to test the students' capabilities of critically analyzing dynamics and control related problems. The official exam is evaluated on a 0-25/30 scale and is complementary to the report evaluation.

Main goal of the course is to enable students to analyse and to design electronic circuits and systems.
The course applies the concepts of frequency-domain and time-domain analysis, noise investigation, and performance assessment to electronic components and circuits, for designing and developing electronic boards and products. The course presents and exploits both analog (transistors and operational amplifiers) and mixed-signal (samplers and ADC/DAC converters) electronic components, in either general-purpose configurations (e.g., voltage-mode operational amplifiers and basic ADCs) or advanced implementations (e.g. current-feedback amplifiers, multi-step ADCs, sigma-delta modulators, etc.).
The course provides the theoretical methodology and the practical skills to understand how a circuit works (moving from the electronic schematics to hand computations of frequency-domain and time-domain performance) and vice versa from high-levels specifications and requirements, to component selection, schematics design, and component sizing. Each step is explained both theoretically (“lesson” classes) and practically (“exercise” classes), through the study of real circuits and case-studies.

Knowledge and understanding (Dublin Descriptor 1)
At the end of the course and after successfully passing the exam, the student:
knows how to analyse analog and mixed-circuit electronic circuits, and to understand the impact of component sizing on performance;
knows how to design feedback circuits and to compute frequency response, impedances, stability, and noise performance;
understands how to sample, hold, and convert analog signals to digital samples, for proper static and dynamic performance, and how to select the suitable ADC and DAC components.
Applying knowledge and understanding (Dublin Descriptor 2)
At the end of the course and after successfully passing the exam, the student:
is able to apply the acquired understanding to assess operation and performance of given electronic circuit schematics;
is able to apply the acquired knowledge and skills to design electronic circuits from specifications and block diagrams;
is able to discuss pros and cons and performance trade-offs among different circuital implementations.

Noise
Basics of noise: distribution, spectrum, power, and effective value.
Electronic noise: shot, thermal, flicker, and burst noise.
Noise Equivalent Generators, equivalent Bandwidth, Noise Figure and Temperature.
Case-studies of noise analysis in electronic circuits.
Amplification
Operational Amplifiers: parameters, feedback and virtual ground, main configurations.
Frequency-domain response and stability of circuits: analysis, diagrams, compensation methods.
Advanced components: instrumentation (INA), isolation (ISO), current-feedback (CFA), current-mode (CMA, Norton), transconductance (OTA), and Fully-Differential (FDA) amplifiers.
Component data-sheets and case-studies of circuits employing amplifiers.
Sampling
Basics of sampling: time-domain and frequency-domain analysis, aliasing, Shannon theorem.
Sample-and-Hold circuits: static and dynamic errors, compensation techniques, performance.
Advanced components: role of feedback, stability and precision issues, speed-accuracy trade-offs.
Analog multiplexers and digital potentiometers: parameters, timing, acquisition techniques.
Conversion
Digital-to-Analog converters (DAC) and Analog-to-Digital converters (ADC): basic architectures, conversion time, speed/accuracy trade-off, timings.
Dynamic performance: FFT spectra, SNR, SiNAD, ENOB, THD and IMD distortions.
Advanced ADCs: interpolation, folding, half-flash, multistep, time-interleaving architectures.
Over-sampling Sigma-Delta modulators: noise-shaping, SNR and bit improvements, components.
Components data-sheets and case-studies of circuits employing S&Hs, muxes, ADCs, and DACs.

Prova scritta obbligatoria, senza prove in itinere
Prova orale obbligatoria
Students are required to pass a WRITTEN test, followed by a final VIVA (oral) test. The written test aims at assessing student’s understanding of circuit analysis methodologies and design criteria provided through the course (Dublin Descriptors 1 and 2). The viva test aims at evaluating the student’s knowledge and understanding about the overall course topics (Dublin Descriptors 1 and 2). The date of the written test will be published on-line on the POLIMI webpage. The viva test will be held usually within 1-2 weeks after the written test, once the teacher will have evaluated the written tests and informed the students.
The WRITTEN test lasts 3 hours, during which students work autonomously (no chat, no smart-phones, no text books, no lesson slides) on 5 different circuits, to be either studied, sized, or designed; each circuit has two questions to answers with computations, graphs and text. Soon after the written test, the teacher will send the detailed solutions to students by email, for self-assessment. Those students wishing to withdraw their test may send an email to the teacher, asking not to evaluate it (e.g., if she/he realises that the test does not reach her/his target quality). Then, the teacher evaluates all written tests, fills in an excel file with a grade (from 0.0 to 1.0) per each question, and the final mark (from 0 to 30 out of 30) for the written test, and emails it to all students. Only students with a ranking higher than 17/30 can attend the viva test. The email also provides dates and locations among which the student can choose from to give the final viva test.
At the VIVA test, each student will be asked with questions covering all course topics, mainly focused on assessing the theoretical know-how acquired (Dublin Descriptor 1) and how to apply it to actual electronic circuits (Dublin Descriptor 2) for both analysis and design. The viva usually lasts 20-30 minutes per student. The viva ends with a final mark (from 18 to 30 cum laude out of 30) if passed, or a fail (the student has to redo the complete exam). Usually, the viva test results in an increment or decrement of the written test’s mark up to +/- 4 (out of 30). Therefore, students may consider to withdraw after the evaluation of the written test or during the viva test, if the grade is far away from their expectations (e.g., with a written test grade of 22/30, when the desired mark is higher than 28/30).

Franco Zappa, Electronic Systems, Editore: Esculapio, Bologna, Anno edizione: 2012, ISBN: 9788893850858 www.editrice-esculapio.com/zappa-electronic-systems/

Design of embedded computer systems. Computer architectures and system components for embedded and industrial applications. Microcontrollers and specialized microprocessors. Parallel and serial bus systems. Datacommunication in industrial environments. Analog/digital interfaces.

Il corso affronta le principali questioni teoriche relative all'innovazione socio-tecnica (quali i concetti di base dell'innovazione tecnologica, le dinamiche di diffusione delle innovazioni, i limiti del determinismo tecnologico, l'agency degli oggetti tecnologici, la flessibilità interpretativa della tecnologia, genere e tecnologia) e analizza esempi rilevanti di tecnologie d'avanguardia che hanno un impatto sulla società e sulla vita umana, come l'Internet delle cose, la fabbricazione digitale, la realtà aumentata e l'intelligenza artificiale. Anziché focalizzarsi sul loro funzionamento, le prestazioni e i problemi tecnici, il corso affronterà le questioni etiche che esse sollevano e gli impatti sociali che producono.

Technical issues have been playing a relevant role in forensic practice and represent an important aid to those who administer Justice. The typical engineering methods represent a large part of the forensic sciences, and technical experts' role has steadily gained importance in the last decades.
Justice has always sought certain means to assess the factual truth to render fair verdicts. To the investigators and triers of facts, science and techniques appear like the optimal tools to provide a certain representation of facts, based on scientific and irrefutable data.
Unfortunately, science and technique have their own limitations and they can only provide limited information on the events they model. This means that every decision taken based on a scientific judgment of facts has an intrinsic risk of being not fully correct, and it is important, from both the technical and ethical points of view, to consider such limitations.
The objective of this course is to frame the role of the technical experts inside the judicial proceedings and explain how the implicit doubts in the result of every piece of evidence collected through scientific and technical activities shall be quantified and presented to the triers of facts to explain them the risk of a wrong decision.
In this respect, forensic metrology is the branch of forensic science capable of providing a pondered answer to these issues not hiding the important, implicit limits of every technical assessment of the facts to the triers of facts, thus avoiding ethical and penal implications and will be covered by this course.
According to the topics covered by this course, it appears to be in line with UN SDG 16.

After attending this course, students are expected to gain basic knowledge of the legal proceedings, and in particular that required to serve as technical experts, both those appointed by the judge and those appointed by the parties.
They will also build competencies on how to report the results of their technical activities, so that they can be fully understood by the legal counterpart (judges, prosecutors, lawyers, jurors), including the implications related to the limited validity. This part will be covered from a theoretical perspective, providing the technical tools needed to identify the sources of uncertainty and quantify their effects on the validity of the provided expertise as well as the risk of issuing a wrong decision based on such expertise. Practical cases will be then presented and the students will be involved in simulating possible cases in team works.
The Dubliner descriptors associated with this course are DD1, DD2, DD3, and DD4.

The following main topics will be covered:
An overview of the legal systems and legal proceedings
The role of the technical experts inside the legal proceedings
The validation of the results returned by the scientific investigations (in the case of crimes) or scientific analysis of the issues that triggered the legal proceeding (n the case of litigations)
The concept of uncertainty in the expression of experimental results
Methods to evaluate the risk of wrong decision when the decision is based on experimental results
The method for ensuring scientific validity to forensic-science methods
All the above points will be covered also by referring to real cases that will be extensively discussed during classes, showing how a more correct evaluation of the scientific evidence might have avoided cases of miscarriage of justice.

Prova orale obbligatoria
Progetto/i - Elaborato/i facoltativo (a scelta dello studente), individuale, senza revisioni periodiche, con presentazione/discussione finale
The evaluation consists of a final oral test, during which the students will be asked to present and discuss the topics covered during the course and show their ability to apply them to a hypothetical case study (DD1, DD2, DD3, and DD4).
If the students wish to prepare a written report on a case of their knowledge, analyzing and discussing how the scientific evidence contributed to the solution of the case, the discussion of such a report will be part of the exam and will replace one of the questions asked. The report can be prepared and submitted prior to the exam, but its submission is not mandatory. There is no penalty for not submitting such a report.
In general, the student should prove its ability to discuss the requirements scientific evidence must satisfy, the capability of identifying the origin of uncertainty contributions in the different steps in which scientific evidence is formed, evaluating them and presenting them to the trier of fact, in order to present and quantify a possible doubt about the validity of the collected evidence.

Alessandro Ferrero, Veronica Scotti, Forensic Metrology: An Introduction to the Fundamentals of Metrology for Judges, Lawyers and Forensic Scientists, Editore: Springer, Anno edizione: 2022, ISBN: 978-3-031-14618-3 https://link.springer.com/book/10.1007/978-3-031-14619-0

This course deals with the application of ethical theories to problems created, aggravated or transformed by technology. This course aims at showing how the decision to develop a technology (meant at large), the processes of its design, development, management, control, and production are inherently moral. It is intended to give students a chance to reflect on the ethical, social, and cultural impact of technological applications (in particular AI).
The course includes lectures by the instructor and invited lecturers; class participation will be expected, and students should apply what they learn through reading, lectures, and class discussions by looking at current events through an ethical lens.

Dublin Descriptors
Expected learning outcomes
Knowledge and understanding
Students will:
Acquire a broad perspective on the ethical and social impacts and implications of science and technology;
Be acquainted with normative ethics and normative argumentation;
Learn how to recognize and analyze ethical and social aspects and issues inherent in some technological applications;
Be able to understand how technical problems are inherently connected to a social dimension within a socio-technical perspective.
Applying knowledge and understanding
Students will:
Be able to use critical skills in clarifying and ethically analyzing case-studies involving technology;
Be able to apply ethical theories to problems created, aggravated or transformed by current technologies;
Be able to explore and assess possibilities for solving or diminishing existing and emerging ethical and social problems.
Making judgements
Students will be able:
To autonomously analyze the ethical issues of a technology and to develop a critical analysis on that;
To evaluate and select the appropriate knowledge in the effort of elaborating and justifying a philosophical argument on a topic autonomously selected.
Communication
Students will learn to:
Exercise and improve their skills in a critical writing;
Present in an effective way the results of their research, being able to justify their choices.
Lifelong learning skills
Students will:
Be better prepared to their future professional life in an ethically and socially responsible way;
Be able to analyze problems through an ethical lens.

The course will cover different topics both from a theoretical and a more practical point of view. We will start with a broad analysis of the concept of responsibility, in particular in an engineering perspective, and of normative ethics and its tools. We will discuss ethical questions in the design of technology with a focus on Design Ethics and its Social Ethics paradigm. Then ethics in IT-configured societies will be discussed and technology as the instrumentation of human action will be presented. Topics will include, for instance, the novel framework of Responsible Research and Innovation, Value-Sensitive design, the ethical aspects of technicolgical risks, the relationship among sustainability, ethics and technology, information flow, privacy, and values and uncertainty
Students will be supervised in class sessions in the development of the individual final essay in order to meet the standards required by scientific publications.

Progetto/i - Elaborato/i obbligatorio, individuale, senza revisioni periodiche, con presentazione/discussione finale
Type of assessment
Description
Dublin descriptor
Collective presentations
Students are encouraged to:
Develop an argument in which the presented claims are justified by the use of ethical argumentation and the relevant literature;
To present this argument in an oral presentation in class in front of the instructor and their student colleagues.
1, 3
4, 5
Individual written essay
The individual final essay must show that the student has the capacity to read and carefully study the suggested bibliography and the ability to apply it to case-studies;
The individual final essay will be about the ethical issues associated to technologies that require to have developed critical skills in analyzing technical problems with the tools provided by ethical theories and argumentation.
1, 2
3, 4, 5

Ibo van de Poel, Lambèr Royakkers, Ethics, Technology, and Engineering: An Introduction (second edition), Editore: Wiley-Blackwell, Anno edizione: 2023, ISBN: 978-1-119-87943-5
Mattia Andreoletti, Daniele Chiffi, Philosophy of Risk, Editore: Cambridge University Press, Anno edizione: 2027
Note: If not yet published, an advanced draft of the book will be made available to students.
Behnam Taebi, Ethics and Engineering. An Introduction, Editore: Cambridge University Press, Anno edizione: 2021, ISBN: 9781316628409

The course aims to prepare graduate students in scientific and technical disciplines to reason about the ethical consequences of their work, with specific reference to the impact on the environment. Specifically, the first part of the course will introduce students to important topics in the ethics of climate change: e.g., what principles should guide our preservation of the human species? What are the possible policy responses to climate change and how should they be evaluated from a moral standpoint? What, if anything, do we owe to the natural environment and to non-human animals? In the second part, we will bring the discussions of the first part to bear on the question of what constitutes 'due diligence' for technologists in light of climate change, using case-studies from scientific and technical sectors to illustrate the issues at stake. This will help bring out the dimensions of responsibility that are incumbent upon technological production today.

The goal of the course is to develop skills in the analysis, design, and verification of critical systems (with particular emphasis on concurrency and real-time aspects), by exploiting formal methods.

Dublin Descriptors
Expected learning outcomes
Knowledge and understanding
Students will learn the principles and paradigms that guide the specification, design and verification of modern real-time, safety-critical systems.
Applying knowledge and understanding
At the end of the course students will be able to identify the critical issues in real-time systems design, and to apply the specification and verification techniques they learnt to solve them.
Making judgements
At the end of the course students will be able to evaluate and compare the various alternatives in specifying and verifying a distributed system.
Communication
Students will learn how to describe important topics in the filed, including recent scienftific research, and how to present their own results.
Learning skills
At the end of the course students will be able to autonomously learn new specification and verification techniques, together with selecting and using new verification toolkits.

Theory and practice of specification and verification of real-time systems, with particular focus on transition systems, timed automata, model checking and temporal logic;
Hoare’s method for program specification and verification.

Prova scritta facoltativa (a scelta dello studente), senza prove in itinere
Prova orale condizionata (a scelta del docente)
Progetto/i - Elaborato/i facoltativo (a scelta dello studente), di gruppo, con revisioni periodiche, senza presentazione/discussione finale
A homework project, asking to model and verify a small real-time system, will be assigned to be developed either individually or, preferably, in small groups of 2-4 people. The homework must be completed by mid-July, and it is mandatory for the students who participate in the Politecnico-UIC program.
Alternatively, students may give a public presentation of a tool, on the topics of the course, to be discussed with the instructor and the other students during regular class hours.
The presentation can be prepared in groups of 3–4 students. The number of available presentations is, however, very limited, so only 5 or 6 groups will be accepted.
Students who cannot give the presentation or prepare the homework will have to pass a (closed book) written test, possibly followed by an oral exam at the instruction's discretion. The test will deal with theory and exercises on the topics of the course.
The final score in every case will be given in 30ths for the Politecnico and in American grades (A through D) for UIC students.
Dublin Descriptors
Expected learning outcomes
Knowledge and understanding
Students will learn the principles and paradigms that guide the specification, design and verification of modern real-time, safety-critical systems.
Applying knowledge and understanding
At the end of the course students will be able to identify the critical issues in real-time systems design, and to apply the specification and verification techniques they learnt to solve them.
Making judgements
At the end of the course students will be able to evaluate and compare the various alternatives in specifying and verifying a distributed system.
Communication
Students will learn how to describe important topics in the filed, including recent scienftific research, and how to present their own results.
Learning skills
At the end of the course students will be able to autonomously learn new specification and verification techniques, together with selecting and using new verification toolkits.

Christel Baier, Joost-Pieter Katoen, Kim G. Larsen, Principles of Model Checking, Editore: Mit Press, Anno edizione: 2008, ISBN: 978-0262026499
Note: Main reference book. It covers model checking and timed automata. Chapters 1-7 and 9.
Furia, C.A., Mandrioli, D., Morzenti, A., Rossi, M., Modeling Time in Computing, Editore: Springer, Anno edizione: 2012, ISBN: 978-3-642-32332-4
Note: Additional material in temporal logic and automata.

Hypothesis Testing (maximum a-posteriori probability, Neyman-Pearson testing, minimax testing)
Signal Detection: uncoded modulation, coded modulation, ISI channels, Viterbi Algorithm, BCJR algorithm
Parameter estimation: Fisher information and Cramer-Rao Bound, minimum variance unbiased estimation, Maximum-Likelihood estimation, Least-Squares, Bayesian estimation
Bayesian inference, sum-product algorithm, and belief propagation
The Approximated Message Passing (AMP) algorithm
Wiener filters and Kalman filters

Learning Objectives. The course fits into the concentration in Supply Chain (SC) Management of the overall curriculum in Management Engineering, and it contributes to some general learning goals. The course focuses on developing the following capabilities. To identify trends, technologies and key methodologies in the realm of SC Planning. To design approaches, tools and methodologies grounded on a scientific and engineering-based approach (analysis, learning, reasoning, and modeling skills coming from a solid and rigorous multidisciplinary background) to face SC planning problems and opportunities in service-related and industrial environments.
To interact in a professional, responsible, effective and constructive way in a working environment, motivating also team members.

Expected Learning Outcomes Upon completion of this course, the students are expected to demonstrate ability: To identify trends, by assessing the impact of planning activities over SC decisions, in the light of the increasingly growing need to deliver bold SC plans to react to a continuously changing competitive scenario; To design and develop effective global SC plans aligned to corporate strategic plans and critical success factors of the addressed business area. The students will be able to contribute to the main SC decisions ranging from SC configuration to short-term delivery plans for the different SC partners. To successfully interact with professionals from different disciplines and various backgrounds, by identifying the main planning issues and outlining the cause-effect relationships that drive SC performance.

Topics Covered
Amazon, Apple, McDonald's, Procter and Gamble (P&G), Unilever. What do they have in common?
Most of them share outstanding places in Fortune 500 ranking, and all of them – according to Gartner [1] – represent the Masters category, which recognizes sustained SC excellence. The Masters continue to demonstrate advanced lessons for the SC community, and they highlight the accomplishments and capabilities of long-term SC leadership. While the wave of Covid pandemic was sweeping the world, SC disruptions and resilience gained attention in daily news. However, behind resilience and agility – to navigate turmoil effectively and successfully – SC Masters have been restructuring their organizations, transforming business models and redefining markets by blending well-established capabilities with emerging innovations. In the end, SC masters have succeeded in reshaping their own SCs through tremendously effective planning initiatives.
How should we care about SC planning?
In a search for competitive differential advantage, many managers of world–class organizations around the globe have come to realize that differentiating on processes is more sustainable than differentiating on products. Indeed, SC planning is the forward-looking process of coordinating assets to optimize the delivery of goods, services and information from supplier to customer, balancing supply and demand [2]. SC planning looks after current processes – including the ones originated in pure service industries – and determine methods for orchestrating the execution flow, the information flow and the financial flow in SCs.
Who are SC planners?
SC planners have the ability to look at the whole SC from upstream to downstream with both short- and long-term mindset. They also troubleshoot breakdowns along the SC and respond fluidly to unexpected events. We now live in the age of the digital SC, which makes it easier than ever before to understand the intricacies of SCs. SC planners are tech-savvy and they feel comfortable working alongside the world of “machines”: they combine technical and business knowledge (for example the ability to handle forecasting and logistics) with collaboration and communication skills in writing and verbal conversation with a wide range of characters from dock workers to high-level executives and business owners. SC planners do not work in a silo and they do not wait for someone else to do what needs to be done. They are proactive, and they bring out the same drive in others. In the end, SC planners are leaders in the seminal meaning of people who cause others to want to travel with them [3]: they show the way to themselves, to others and to teams.
How should we train the SC planners of the future?
When it comes to SC planning, CEOs typically feel in the dark and they take a back seat, thus overlooking that if an organization really nails its SC planning, it can react to disruptions before they even happen, and quickly transform strategy into execution. We must train SC planners in such a way that they are able to steer their organizations to respond more quickly to market development, and they can lead their organizations on a journey toward prolonged success by forging a greater synergy with their CEOs.
There are at least three major topics SC planners should make their CEOs aware of, before dipping their toes into SC planning: (i) whether company’s SC has been consciously established and improved through a decision-making process, or it has merely evolved, which reveals that probably SC configuration has not received enough attention and your logistics managers do not know why warehouses are located where they are; (ii) whether business and SC strategies are misaligned, as SC plans have much more to do with customers and service than suppliers and cost, which means that a growth business strategy and an aggressive cost-cutting SC strategy do not match; (iii) whether some customers are over-serviced – and then less profitable – as service levels are based on perceived customer needs, but you failed to check whether the reality matches that perception.
What SC planners should bring in their toolboxes?
Some widespread gossip whispers that SC planners are ideal positions for analytical nerds who spend their idle time at work playing Tetris and who would rather solve a Rubik’s cube at the weekend than do anything else. On the other hand, the SC planners of the future are outgoing leaders with outstanding ability to steer effective SC designs: they keep SC plans aligned to business plans, and they improve service levels while reducing costs at once. Hence, a typical SC planning toolbox include: Sales and Operations Planning (S&OP), Collaborative Planning Forecasting and Replenishment (CPFR), Vendor–Managed Inventory (VMI), Direct Delivery Systems (DDS), Available and Capable to Promise (ATP/CTP), multi-plant capacity planning, demand and inventory planning and factory planning.
Syllabus
To properly match the requirements of both Management Engineering (ME) and Management of Built Environment (MBE) courses of study, classes have been arranged into 14 main sessions with a typical format of 3 – 4 hours in a row once a week (typically on Monday morning). The course syllabus has been built around the best path we should set out to properly train the SC planners of the future.
Students will be confronted with various real-life, real-time dilemmas that require cross-functional understanding, collaboration, and leadership.
Sessions L1 to L11 are related to lectures and each of them matches with several case studies, to be practiced in the corresponding session in a flipped mode fashion. Case-studies play a pivotal role in achieving a relevant portion of learning objectives [1], and they will be corroborated by some guest speeches of industrial directors and planners.
The introductory session (L1) is devoted to SCs and SC planning and the presentation of SCOR model (Source, Make, Deliver and Return), while Sessions L2-L4 refer to sustainable, strategic and global SC configuration. L5 refers to the evolution of S&OP through IBP. Then, Session L6 is related to emerging trends and recent advances in make vs. buy SC strategy and the role of SC finance supporting decisions of corporate strategy.
Session L7 is releted to risk mitigation strategies aimed at making SCs resilient to economic and geopolitical disruptions. Session L8 and L9 focus on the impact of customer service and marketing strategies on SC planning process. Session L10 addresses the role of “x”-shoring strategies (like re-, back-, near-) as a response to economic and geopolitical risks.Session L11 outlines the impact of product design on SCs and the interactions between NPD process ad SC management process.
Finally, sessions T1 and T2 include additional practice respectively on sessions L1-L6 and L7-L11, and (upon request) they can be graded as Mid-term exam and Early-bird exam.
The course in a nutshell
L1 Execution, information and financial planning. SCOR model
L2 Strategic SC planning in action: Fisher & Hau Lee Models
L3 Using AI for SC planning
L4 Integrating SC planning and marketing
L5 Global SC planning & distribution networks
L6 SC planning via IBP
L7 S&OP Game
L8 Upsteam SC planning & SC finance
L9 Sustainabile SC planning for circular economy
L10 Mitigating SC risks and creating resilience
L11 Aligning SC planning and NPD process
[1] To see how a typical case-based session should look like, try: https://www.youtube.com/watch?v=p7iwXvBnbIE.
The instructor’s standpoint is well highlighted here: https://www.youtube.com/watch?v=eA5R41F7d9Q&t=44s.
[1] https://www.gartner.com/en/supply-chain/research/supply-chain-top-25
[2] https://www.gartner.com/en/information-technology/glossary/scp-supply-chain-planning
[3] The leadership role is more about the destination, vision, engagement, emotion. To capture a glimpse of leadership, try: https://www.youtube.com/watch?v=pxBQLFLei70 (2014 commencement speech at UT Austin). A footage is available also with subtitles (and ads): https://www.youtube.com/watch?v=TBuIGBCF9jc.
[4] To see how a typical case-based session should look like, try: https://www.youtube.com/watch?v=p7iwXvBnbIE.
The instructor’s standpoint is well highlighted here: https://www.youtube.com/watch?v=eA5R41F7d9Q&t=44s.

Prova scritta facoltativa (a scelta dello studente), con prove in itinere
Prova orale facoltativa (a scelta dello studente)
Valutazione continua facoltativa (a scelta dello studente)
Grading
All assignments are due by 11:59 PM CET on the due date, usually the day before the corresponding session. For example, when a session is scheduled on Friday, assignments are due by Wednesday at midnight. When a session is scheduled on Monday, assignments are due by Thursday at midnight To accomplish our learning objectives, we use a variety of techniques: readings, written assignments, videos, and discussion. We will often refer to and apply the tools and frameworks from the class when analyzing cases, discussing issues, and treating SC planning problems. One of the real benefits of the course is that you can enhance your learning through productive discussion with peers in the classroom. Contributions are evaluated on the basis on your willingness to constructively challenge and discuss the concepts of SC planning through presentations, case assignments and in-class exercises.
Case discussions are useful as you are supposed to put yourself in the shoes of the executives and take a position on what you would do. Questions have been provided that lead you through important issues to examine. For every question, always consider the pros and cons of each answer, as there are no easy solutions in SC planning. Considering both sides will push you to reconsider the assumptions that often are embedded in each problem setting. Another important part of case analysis is to look at any numerical information provided. The numbers can and should guide you towards both the successes and failures of the company. This portion of the class may involve breakout sessions for teams to generate strategies and tactics from individual analysis for class discussion. Whenever you provide an assessment, try to frame your thinking about the criteria of interest. My experience taught me that disagreements arise since the basis for the decision was predicated on different criteria and therefore agreement became impossible. Being articulate about criteria allows you to speak with each other rather than past each other.
Class participation is scored as a function of the quality of your contributions (i.e. does your comment move the class discussion forward) rather than the quantity of comments. Students are encouraged to refrain from prolonged or misdirected monologues, statements of common knowledge, and confrontations that are not directed at the issue at hand.
The table below summarizes all assignments along with the corresponding grade points. Any item missing will be treated via an oral test taken after the end of the course, by absorbing the corresponding weight.
Item Type Weight Mode
Mid-term Exam Individual 30% Computer aided test
Early bird Exam Individual 30% Computer aided test
Alternatively,
One-shot final exam Individual 60% Comprehensive oral exam (*)
Participation and In teams 40% Content 25%
case discussions Presentations 15%
(*) 100% for students who did not embark into developing their own case and/or did not participate in the sessions devoted to case discussions
Bibliography
Main textbooks
Jacobs, F.R., Chase, R. B., 2018, “Operations and SC Management”, McGraw-Hill.
Cigolini, R., Franceschetto, S., 2021, “Cases in SC Management”, Mondadori
Additional textbooks (in alphabetical order)
Christopher, M., 2016, “Logistics and SC management”, Pearson Education.
Correa, H.G., 2014, “Global SC management”, Atlas
Gattorna, J., Ellis, D., 2019, “Transforming supply chains: realign your business to better serve customers in a disruptive world”, FT Series
Harrison, A., Van Hoek, R., 2014, “Logistics management and strategy: competing through the SC”, Pearson
Ivanov, D., Tsipoulanidis, A., Schoenberger, J., 2019, “Global SC and operations management”, Springer
Johnsen, T., Howard, M., Miemczyk, J., 2014, “Purchasing and SC management. A sustainability perspective”, Routledge.
Mangan J., Lalwani, C., 2016, “Logistics and SC integration”, Wiley
Rushton, A., Oxley, J., Croucher, P., 2017, “Handbook of logistics and distribution management: understanding the SC”, Kogan
I., 2007, “Logistics and SC integration”, Sage
Shah, J., 2016, “SC management: text and cases”, Pearson
Shapiro, J.F., 2007, “Modelling the SC”, Brooks/Cole, Cengage Learning.
Stadtler, H., Kilger, C., 2005, “SC management and advanced planning”, Springer
Waters, D., Rinsler, S., 2014, “Global logistics: new directions in SC management”, Kogan
Videos
The next few videos may represent a thoughtful introduction to some key topics of the course:
SC Planning @ Starbucks: https://www.youtube.com/watch?v=ElYNhGbOTOQ
SC planning via SCOR model: https://www.youtube.com/watch?v=VrX2Qf0OT2M
SC planning via SCOR again: https://www.youtube.com/watch?v=sL0vKtpwWP0
What supply planning is: https://www.youtube.com/watch?v=4iI6La7-U2o
Integrated SC Planning: https://www.youtube.com/watch?v=fM4xKfLZhFI
A digital SC for the future: https://www.youtube.com/watch?v=HKnpiDiCe2s
Then, just for the sake of warming you up before starting, you might as well take a glance here.
A look around the notion of SCs & SC Management:
https://www.youtube.com/watch?v=Mi1QBxVjZAw&t=11s
https://www.youtube.com/watch?v=lZPO5RclZEo&t=2s
https://www.youtube.com/watch?v=4-QU7WiVxh8
https://www.youtube.com/watch?v=ZuQ200JAViA&list=PLCD3E338A3E58E906&index=7
https://www.youtube.com/watch?v=YbM_LydRlnM
SC strategies & the service sector: https://www.youtube.com/watch?v=mPkgkwb08GU
SC transformation @Intel: https://www.youtube.com/watch?v=3bE4bHBVB6k
SC-related positions, jobs, roles etc.:
https://www.youtube.com/watch?v=Mh8KCRcQtRM
https://www.youtube.com/watch?v=HN5dDOGgKVA
https://www.youtube.com/watch?v=7qGAIaKqAuE
https://www.youtube.com/watch?v=bQKE5Uu5TNo
https://www.youtube.com/watch?v=O6ry5U9pHqE
https://www.youtube.com/watch?v=kkbc2PUxpY0
Some meaningful examples:
Amazon (old) SC: https://www.youtube.com/watch?v=Y-lBvI6u_hw
IKEA (unique) Supply Chain: https://www.youtube.com/watch?v=DBrl356VhqQ
Walmart SC: https://www.youtube.com/watch?v=yZC4neLax5o
Relevant journals for further readings (in alphabetical order)
IJLDM: https://www.emerald.com/insight/publication/issn/0960-0035
IJOPM: https://www.emeraldgrouppublishing.com/journal/ijopm
IJPE: https://www.journals.elsevier.com/international-journal-of-production-economics
IJPR: https://www.tandfonline.com/toc/tprs20/current
JOM: https://onlinelibrary.wiley.com/journal/18731317
JPSM: https://www.journals.elsevier.com/journal-of-purchasing-and-supply-management
JSCM: https://onlinelibrary.wiley.com/journal/1745493x
PP&C: https://www.tandfonline.com/loi/tppc20
SCM: https://www.emerald.com/insight/publication/issn/1359-8546
Related Institutions other than Universities
APICS: http://www.apics.org/
CIPS: https://www.cips.org/

Mathematical modeling and simulation of vehicles in 6 degrees of freedom. This includes mathematical modeling of ships, semi-submersibles, aircraft, autonomous underwater vehicles (AUV), uncrewed surface vehicles (USV), and uncrewed aerial vehicles (UAV). Introduction to aerodynamics, hydrodynamics, sea loads, and mathematical modeling of the environment (waves, ocean currents, and wind). Kinematics (Euler angles and unit quaternions), transformations, rotation matrices, geographical and body-fixed coordinates systems, rigid-body kinetics, and vectorial mechanics. Methods for designing, programming, and implementing guidance, navigation, and control (GNC) systems for marine craft and aircraft. This includes simulation and testing of motion control systems during failure situations and for varying environmental loads. Emphasis is placed on classical guidance systems, including line-of-sight (LOS) methods and path following. Applied control theory, optimization, state estimators (Kalman filtering), nonlinear observer theory, PID control with extensions to nonlinear systems, Lyapunov methods, sliding-mode control, feedback linearization, backstepping designs, and passivity-based methods. Autopilot design, dynamic positioning, attitude stabilization, roll damping, altitude and depth autopilots, sensor and navigation systems, and wave filtering. Observers and error-state Kalman filter for integration of global navigation satellite systems (GNSS) and inertial measurements (attitude rate sensors and accelerometers).

The course is conceived as an experiential-based learning process, where students acquire frameworks, models, and tools to develop their business mainly from the experience and advice of practitioners, i.e., entrepreneurs, venture capitalists, business angels, incubator/accelerator managers, with whom the course is designed. The faculty will provide a core of preliminary concepts through frontal lectures on high-tech start-ups’ creation and funding, after that the students will start to work in teams on their own business models, receiving inputs and feedback from practitioners, who participate as guest speakers, and throughout tutoring sessions.
The course fits into the overall program curriculum pursuing some of the defined general learning goals. In particular, the course contributes to the development of the following capabilities:
Understand and design solutions, applying a scientific and engineering approach, in relation to the setting up of a new business, the design of its business model, and the identification of the proper funding channels
Interact in a professional, effective and constructive way in teams and with external practitioners

At the end of the course, the students will be able to:
Comprehend the functioning of the entrepreneurial ecosystem (DdD1 - Capability of knowledge and understanding; DdD2 - Capability of applying knowledge and understanding)
Understand what is a business model and its building blocks (DdD1 - Capability of knowledge and understanding; DdD2 - Capability of applying knowledge and understanding)
Identify the key challenges for the creation of high-tech start-ups and the funding mechanisms available, and understand when it is appropriate to use each of them, according to the specific start-up’s industry, funding requirements and stage of the life cycle (DdD1 - Capability of knowledge and understanding; DdD2 - Capability of applying knowledge and understanding).
Through the project work, the students will also be able to:
Test and validate the business model in the real world with potential customers (DdD3 - Capability of making judgments; DdD4 - Capability of communication)
Write an effective business proposal to ask for funding (i.e., to business angels, venture capitalists, incubator programs..) (DdD3 - Capability of making judgments; DdD4 - Capability of communication)
Effectively work in teams (DdD3 - Capability of making judgments; DdD4 - Capability of communication)
Interact with practitioners of the financial sector (DdD4 - Capability of communication)
Summarize and present the results achieved in the business proposal in front of potential investors clearly and convincingly. (Capability of communication)

The course program includes the following topics:
The creation of high-tech start-ups. Entrepreneurship in high-tech industries and high-tech start-ups in Italy and in the world
The development of of high-tech start-ups, and their performances, including the success factors
The Business Model: what is a business model and how to fund it through equity capital, debt capital and public subsidies
How to design the Business Model: the value proposition and business model testing; the use of the Business Model Canvas framework; organizational structure and management; market strategy and market positioning; competitors’ analysis; how to protect competitive advantages; business model risk assessment
The financing of high-tech start-ups: a series of guest speakers, including entrepreneurs, venture capitalists, business angels, incubator/accelerator managers, will bring their direct experience through success stories regarding the funding (and creation) of high-tech start-ups (entrepreneurs) and explaining their decision-making and investment process (investors)
How to design and perform an effective elevator pitch.

Progetto/i - Elaborato/i obbligatorio, di gruppo, senza revisioni periodiche, con presentazione/discussione finale
Valutazione continua condizionata (a scelta del docente)
Students must identify a new business idea based on an innovative technology (e.g., the results of current scientific research) or an innovative way of doing business in an incumbent industry (e.g., an innovative business model), form an entrepreneurial team, develop a document describing the business model, and prepare an elevator pitch.
The examination consists of the evaluation, by the faculty and a commission of practitioners (including guest speakers of the course), of both the business model document and its presentation before the same commission.
In the examination, students must demonstrate that:
They have a good understanding of the challenges involved in creating and funding a new start-up and of how financial intermediaries and other actors of the entrepreneurial and financial ecosystem may support the start-up’s development
(DdD1 – Knowledge and Understanding; DdD2 – Applying Knowledge and Understanding).
They understand the structure and components of a business model and are able to design and develop one tailored to their business idea
(DdD1 – Knowledge and Understanding; DdD2 – Applying Knowledge and Understanding).
They have effectively validated the proposed business model in the real world and are able to critically assess and select the most appropriate funding channels for the venture
(DdD3 – Making Judgements; DdD2 – Applying Knowledge and Understanding).
They are able to summarize, communicate, and defend the key elements of their business model clearly and convincingly in a public setting through an effective elevator pitch
(DdD4 – Communication Skills).
During the tutoring sessions, as well as in the final examination, students must also demonstrate that:
They are able to work effectively within entrepreneurial teams, contributing constructively to the development of the project and the achievement of shared objectives
(DdD4 – Communication Skills; DdD5 – Learning Skills).
They are able to critically incorporate and leverage feedback and advice provided by practitioners and guest speakers to improve their business model and entrepreneurial proposal
(DdD3 – Making Judgements; DdD5 – Learning Skills).

clips on startup creation made available on Beep
Note: Topics:
team formation;
characteristics of entrepreneurial ecosystems;
startup funding;
business models.
Alexander Osterwalder, Yves Pigneur, Business model generation, Editore: John Wiley & Sons, Anno edizione: 2010

Scopo del corso è di offrire una lettura storica del percorso evolutivo dell'impresa europea nei suoi snodi fondamentali, con particolare riguardo all'innovazione tecnologica. In questa prospettiva sarà collocata anche la storia dell'impresa italiana riferita alle esperienze sia delle maggiori imprese private (Fiat, Olivetti, Pirelli) sia dell'impresa pubblica (Iri), soffermandosi infine sulle ragioni della caduta delle imprese di grandi dimensioni e sull'affermazioni di forme di capitalismo "intermedio". Nella sua parte finale il corso insisterà sulla continuità dei caratteri dell'impresa europea, domandandosi se, all'interno di uno scenario post- globale come l'attuale, essa abbia la possibilità di continuare il percorso lungo il proprio asse o se invece emerga l'opportunità di una contaminazione con altre esperienze modulate da differenti contesti storici.

Introduction and motivation, chaos in hybrid systems, Zeno effect, hybrid automata, switched linear systems, introduction to behavioural systems theory, abstraction based controller synthesis, l-complete approximation.

T he course aims for providing an operational and methodologically sound vision on how ICT is used to realise controllers described and synthesised as dynamic systems, on how this activity must be carried out so as to ensure a correct and non ambiguous model-to-implementtation path, on what conceptual tools are needed to this end, on what technologies and physical devices come into play, and finally on how these tools, technologies and devices need coordinating with one another to achieve – with a necessarily interdisciplinary approach – the ultimate goal. Along this path, the necessary industrial and regulatory standards are also introduced, focusing on their systemic motivations and on the development process that they intend to induce. As a complement to the above, the course also provides a synthetic overview of the current state of the art.

Knowledge and understanding
The students will learn about
the functionalities of industrial control systems besides the bare control law;
the formalisms to describe such functionalities;
the methodologies to turn the so described control models into their (software) implementation unambiguously;
the hardware/software architectures to host the realised controls;
the involved technologies and industry standards;
the involved analysis, simulation and assessment tools.
Applying knowledge and understanding
The students will be made capable to
complete a controller model as it comes from the synthesis phase with the description of functionalities that are necessary for its operation and management, but not convenient to take into account in the aforementioned phase;
translate the so completed controller model, up to this point expressed in terms of dynamic systems, into another model that can be realised in algorithmic form unambiguously, but is still abstracted with respect to the host hardware/software architecture;
choose the appropriate mix of technologies, size the architecture just mentioned, and based on the last model above, proceed with the physical implementation of the controller;
employ analysis and simulation tools to carry out and/or support support the activities above.
Making judgements
Besides the above and compatibly with the course level, the students will learn to
judge on the feasibility of implementing a given set of control functionalities - including quantitative specifications when relevant - on a given host architecture, and provide proper advice in the case of problems;
analyse possible (nontrivial) control malfunctions in relation with the controller model that generated the faulty implementation, identifying and correcting possible flaws of that model.
Communication
The students will learn to describe and present their work, adhering to the proper industry standards when this is required/convenient.

Introduction and background
Definitions and notation; scope and purpose of the course; brief recap on feedback synthesis and discretisation; supporting software tools.
Modulating blocks
Standard realisations (minimal series/parallel or "by actions", nonminimal IIR, positional, incremental, mixed-type); saturation management; additive and biasing signals; tracking; incremental locks; management of external forcing inputs; modulating blocks as switching discrete-time systems; application examples
Logic and accessory blocks
Combinatorial and sequential logics; timers and sequencers; auto/manual station; signal generation and routing components; actuator management components (daisy chain, split range, time division output); application examples
The IEC 61131 and IEC 61499 standards
Generalities; function blocks; relatioships with the switching representation of control blocks; consequent implementation guidelines.
Architecture-abstracted representation of a control configuration
The ideal architecture hypothesis, its interpretation and representativeness limits; organisation in subsystems and signals; the three-node model.
Algorithmic translation in the (quasi-)ideal architecture hypothesis
Execution model (periodic or cyclic, by events); timing and I/O management; application examples (modulating acyclic case, modulating non acyclic case, modulating plus logic).
Host architecture and control deployment
The measurement and actuation chain from the control viewpoint; field-interacting and non field-interacting sections; overview of the involved technolgies.
Main sources of non-idealities in real architectures
Generalities; synchromisation and related phenomena; communication delays; operating system artifacts; numeric precision.

Prova scritta obbligatoria, senza prove in itinere
The exam consists of a mandatory written test including multiple-choice questions, open-ended questions, and numerical/algorithmic exercises. With reference to Knowledge and understanding, students are expected to demonstrate their comprehension of industrial control system functionalities, implementation methodologies, hardware/software architectures, enabling technologies, standards, and analysis tools. As for Applying knowledge and understanding, students are required to complete controller models with the necessary ancillary functionalities, translate them into unambiguous algorithmic representations, and analyse implementation choices. Making judgements is assessed through the analysis of implementation feasibility and control operation, and Communication skills through the use of appropriate technical terminology and clear presentation of their reasoning, especially in the open-ended questions.

The purpose of the Image Analysis and Computer Vision course is to study both the foundations on image formation, image analysis, 3D scene reconstruction, and the methodologies underlying the solution techinques to the main problems involved.
Image analysis addresses the extraction of the content of one or several images; Computer Vision addresses the methodologies to obtain a representaton of the observed 3D scene. Optical, geometric, and algorithmic aspects are studied, as well as aspects connected to signal processing and data analysis.
A project allows to closely examine, possibly from a practical point of view, one or more of the discussed topics.

1. Knowledge and understanding: Geometric foundations of the image projection; Image analysis as a signal
2. Applying knowledge and understanding: Use geometric knowledge to devise techniques to interpret an observed scene; Use knowledge on signal processing to develop techniques to extract relevant features from images
3. Making judgements: Given a complex and realistic scene reconstruction problem, try to infer a sufficient set of constraints from the particular circumstances of the problem instance
4. Communication skills: presenting own ideas to the teacher/gruop mates relative to a complex problem to solve, reporting adopted solution and results
5. Learning skills: learning to correlate items from an eterogeneous corpus of knowledge to solve a complex and realistic Computer Vision problem, use foundations to allow lifetime learning in Image Analysis and Computer Vision

0. Introduction.
1. Camera sensors: transduction, optics, geometry, distortion
2. Basics on Projective geometry: modelling basic primitives (points, lines, planes, conic sections, quadric surfaces) and projective spatial transformations and projections.
3. Camera geometry, and single view analysis: calibration, image rectification, localization of 3D models.
4. Multi-view analysis: 3D shape reconstruction, self-calibration, 3D scene understanding.
5. Linear filters and convolutions, space-invariant filters, Fourier Transform, sampling and aliasing.
6. Nonlinear filters: image morphology and morphology operators (dilate, erode, open, close), median filters.
7. Edge detection and feature detection techniques. feature matching and feature tracking along image sequences.
8. Inferring parametric models from noisy data (including outliers), contour segmentation, clustering, Hough Transform, Ransac (random sample consensus).
9. Applications: object tracking, object recognition, classification.

Prova scritta obbligatoria, senza prove in itinere
Progetto/i - Elaborato/i obbligatorio, di gruppo, con revisioni periodiche, senza presentazione/discussione finale
Altro
EVALUATION.
The standard examination consists of two parts: an individual homework (max 10/30 pts) and an application project (max 20/30 pts) on a topic from a list proposed by the teacher (project can possibly be extended to Master Thesis).
A. Written individual homework: study the geometric aspect of the assigned problem, exploit problem specification to derive useful constraints (1,2, 3); implement the solution (including the extraction of the needed image features), while addressing noise and uncertainty on the extracted image features, and evaluate experimental results (3, 5); carefully write a self-sufficient report (4).
B. Assessment of project artifact with written report and presentation: Assessment of the design and experimental work done either individually or in small groups (up to three participants): 2, 3, 4, 5.
Project can be either full or short. Full project evaluation outcome contributes maximum 20/30 pts -possibly, with laude assignment- to the final mark. Short project evaluation contributes maximum 16/30 to the final mark (no laude). In both cases, the submission of a well written, self-explaining report plus a presentation file is required.
If the homework is submitted and a good score is obtained (minimum 8/10), the student can ask to compose his final mark basing only on the evaluation of the full project. A " laude" grade can only be granted if both the Homework and the Project are excellent, i.e., respectively 10/10 and 20/20.
ESCAPE STRATEGIES.
1) Project but no homework: the exam will include a written proof, including difficul exercizes. Final grade ranges up to a maximum of 28/30.
2) Homework but no project: the exam will include a full written proof, including some difficult exercizes. Final grade ranges to maximum of 27/30.
3) No homework, no project: the exam will consist in a full written proof, including some difficult exercizes. Final grade ranges to maximum 25/30.

R. Hartley - A. Zisserman, Multi-view Geometry for Computer Vision, Editore: McGraw- Hill, Anno edizione: 2003
Slides of the course https://webeep.polimi.it/course/view.php?id=1047§ion=2

The course provides the basics for analysis and use of electric and electronic components in power, telecommunication and cybernetic systems. The course also aims to provide basic training in laboratory work for the design and implementation of digital control of electric motors, in data handling/analysis.

The course provides a comprehensive introduction to the design and operation of larger industrial control and safety systems - primarily within the process industry. This includes examples of regulatory requirements and standards that must be followed, typical technical solutions including user interfaces, equipment selection, types of communication, configuration of logical operations in software and hardware, and how the design of systems is affected by cyber security threats. The course also explains how requirements associated with instrumented safety systems are derived in a risk-based approach and how the results affect the technical solutions. Although today’s process industry is the main focus, examples of approaches within, for example, signal systems railway, carbon capture, and nuclear power are given. An introduction is given on how to calculate the reliability of instrumented safety functions and how this is used to make decisions about whether the solution is good enough - also in operation. Based on this, the students should be able to highlight the role of instrumented safety systems as measures to prevent major industrial accidents. Overall, we may refer to this course as a systems engineering course, considering the Wikipedia definition of systems engineering as an interdisciplinary field of engineering and engineering management that focuses on how to design, integrate, and manage complex systems over their life cycles.

At the end of the course the students will be able to:
-anlyze and design complex telecommunication systems based on Internet of Things
-understand and apply complex communication technologies at the very heart of IoT systems
-design communication networks of tiny and constrained devices to enable IoT applications and services
-design IoT systems interconnected with the Internet

1 - Knowledge and understanding
Students will learn how to:
-Analyze complex communication technologies for IoT systems
-Analyze application layer protocols for the IoT
-Analyze management platforms for the IoT
-Identify IoT system components and their relations
-Use operative systems for the IoT
-Use prototyping platforms for the IoT
2 - Applying knowledge and understanding
Students will be able to:
-Dimension and optimize the energy consumption of sensor nodes
-Evaluate the performance of communication technologies for the IoT
-Evaluate the performance of application layer protocols for the IoT
-Evaluate the performance of management platforms for the IoT
-Develop simple applications based on IoT
3 - Making judgements
Students will learn to:
-Understand the fundamental tradeoffs that govern the energy management of battery-operated sensor nodes
-Identify how the fundamental system limitations impact the design of the IoT systems
-Recognize the design space and its degrees of freedom that can be exploited to define communication technologies for the IoT
4 - Communication
Students will learn to:
-describe complex network technologies at different levels of detail

The course will provide the basics on the four major building blocks of the Internet of Things: the reference hardware, the communication technologies, the management platforms and the data storage/processing solutions. The course will start off by overviewing the most common hardware components of sensor nodes, and will then move to addressing the challenges which have to be faced to interconnect and manage such tiny devices. In this field, low power short rannge and long range communication technologies will be reviewd. Finally, the course will describe the main features and requirements of management platform commonly used to orchestrate IoT systems.
Program at a Glance
1-Introduction: the vision of ambient intelligence, application examples, enabling Technologies (Sensor Networks, RFID), networking Building Blocks and Abstractions
2-Hardware Components & Abstractions: sensor node hardware architectures, basics of ADC, energy consumption models for sensor nodes
3-Application layer protocols for the IoT: networking basics, the Constrained Application Layer Protocol (COAP), the Message Queuing Telemetry (MQTT)
4-Communication technologies for the Internet of Things long range solutions: evolution of the mobile radio network to support IoT traffic (LTE-CatM, NB-IoT), examples of Low Power Wide Area Networks (LoraWAN),
5-Communication technologies for the Internet of Things - short range solutions: introduction to capillary network infrastructures, the ZigBee protocol stack (IEEE 802.15.4 PHY/MAC layer, ZigBee Network layer, ZigBee application layer and profiles), Bluetooth and BLE, overview of THREAD and MATTER
6-Radio Frequency Identification: application Scenarios, the Physical Layers of RFID (operation band, transmission fundamentals), Collision Arbitration Standards and Solutions (Tree-based arbitration, frame-aloha based arbitration, the Q-Algorithm, performance evaluation of arbitration protocols)
7-Hands-On Activities: management and prototyping platforms (ThingSpeak, NodeRed, WokWi), analysis of IoT traffic via Wireshark

Prova scritta obbligatoria, senza prove in itinere
Progetto/i - Elaborato/i obbligatorio, di gruppo, senza revisioni periodiche, senza presentazione/discussione finale
The students will be evaluated through two mutually exclusive ways:
(i) in-course challenges
or
(ii) a final written exam on the topics of the course
The students can decide which type of evaluation is the most appropriate for them,
The written exam includes three exercises and a set of questions on the topics of the course. The students may refer to the course web site to check for templates and sample exams from past year's editions of the course.
The in-course challenges consist in developing mini-projects with fixed delivery deadlines throughout the course. The mini-projects will be organized as follows: one written homework with quantitative/qualitative exercises (similar to those discussed during exercises sessions), three practical mini-projects on the development of IoT applications by leveraging the software tools presented during lectures and lab sessions.
The final grade is
-the grade obtained in the final exam, for those students attending the final exam
-the average gade of the 4 in-course challenges, for those students clearing the course with the challenges. In this case, a short final written exam up to 3 additional points can be given to increase the final grade
The mini-projects can be developed by groups of up 2 students.
The written homework must be done singularly
The students will have to prove in the written exam and in the in-course challenges:
-to have clearly understood and acquired to the topics of the course
-to be able to qualitatively assess all the building blocks of a IoT system (hardware, communication technologies, management platforms and data storage/processing)
-to be able to assess the energy consumption of IoT devices
-to be able to dimension and optimize IEEE 802.15.4 networks
-to be able to evaluate the performances of RFID arbitration systems based on Frame ALOHA and binary trees
-to have acquired and understood the working details of short range (ZigBee, Bluetooth/BLE) and long range (LoraWAN, NB-IoT) communication technologies for the IoT
-to have acquired and understood the working details of IoT application layer protocols (COAP, MQTT)
-to be able to manage the reference software (Node-Red, Thingspeak)
-to be able to analyze IoT traffic
-to be able to build up working IoT application or networking solutions leveraging the aforementioned software
-to be able to produce clear and comprehensive documentaion describing the developed project

Location has become a crucial information for a wide range of services, many of which fall in the area of transportation. Modern transportation systems, often referred to as smart mobility or intelligent transportation systems (ITS), rely on information and communication technologies (ICT) to improve safety, sustainability and quality of mobility. Fundamental is the knowledge of position/velocity of vehicles, people and assets, either single entities or aggregates (i.e., traffic flows). Global navigation satellite system (GNSS) is the dominant technology in open sky, but severe propagation conditions (indoor, urban canyons) or critical performance requirements (accuracy, latency reliability) call for the integration with augmentation technologies, based on wireless networks, radar, and inertial systems.
The goal of this course is to teach students the fundamentals of localization, navigation and communication technologies for connected mobility. Specific objective are i) to provide a basic understanding of wireless technologies and processing methodologies for radio localization, including satellite navigation and wireless positioning based on cellular or short-range networks; ii) to investigate location-based services and technologies for smart mobility, focusing on communication systems for road safety and traffic efficiency, traffic monitoring systems, models and processing methodologies for traffic analytics, vehicular networks for cooperative intelligent transportation systems.
Lessons are integrated with seminars by experts from academia and industry. The course includes interactive activities and exercises carried out in Matlab environment with students organized in groups, aimed at the simulation and assessment of localization/mobility systems in practical use-cases. Laboratory activities will be carried out on use-cases and data defined in collaboration with companies of the sector.

Dublin Descriptors
Expected learning outcomes
1. Knowledge and understanding
Students will be able to:

• understand architectures, elements and signal processing methodologies of wireless positioning systems;
• understand sensing, processing and communication technologies for ITS;
• define mathematical models for formulation and analysis of a navigation problem;
• learn dynamic models and tracking filters for mobile scenarios.
  2. Applying knowledge and understanding
  Student will be able to:
• apply the mathematical models to design positioning algorithms and assess the related performance;
• apply dynamic models and tracking filters to practical scenarios of smart mobility.
  3. Making judgements
  Students will be able to:
• identify suited positioning technologies for different application scenarios;
• identify how the network deployment and measurement limitations impact on the system performance.
  4. Communication
  Students will be able to:
• describe the learned technologies, at different levels of details, also comparing them with respect to specific aspects;
• present a project consisting in the simulation of a localization/mobility problem, implementation of a selected methodology and evaluation of the performance (non mandatory);
• interact with other colleagues during laboratories and project discussion.
  5. Lifelong learning skills
  Students will be able to:
• learn the operating principles and potential of new emerging communication technologies for localization and connected mobility;
• learn how to develop a software prototype, assess the performance and validate it by numerical simulations.

Part I - Fundamentals of localization and navigation
Location-based services, location measurements (TOA, TDOA, AOA, RSS), infrastructures and protocols, spatial-temporal reference systems, positioning methods (multi-lateration/angulation, proximity sensing, fingerprinting, dead reckoning, hybrid approaches), navigation equations and algorithms, accuracy and geometric dilution of precision, dynamic models and filters for mobile positioning (Kalman, grid-based and particle filters).
Part II - Wireless technologies for indoor/outdoor applications
Positioning systems based on cellular and short-range networks (cellular, LPWAN, WiFi, Bluetooth, ZigBee, UWB, RF-ID), satellite navigation (GPS, Galileo, GNSS, Beidou) and augmentation systems, hybrid and advanced technologies (passive radio imaging, cooperative and distributed systems for multi-agent localization/tracking), examples and applications.
Part III - Smart mobility systems
Introduction to ITS, fundamentals of traffic modelling and control, technologies for traffic monitoring, processing methods for traffic analytics, communication systems for road safety and traffic efficiency, cooperative ITS (C-ITS), vehicle-to-anything (V2X) communication for assisted/automated driving, examples and applications.

Prova scritta obbligatoria, senza prove in itinere
Prova orale facoltativa (a scelta dello studente)
Progetto/i - Elaborato/i facoltativo (a scelta dello studente), di gruppo, senza revisioni periodiche, con presentazione/discussione finale
The exam consists of a written test (2-3 numerical exercises and questions on the course contents). Students can integrate the written test result with either a project on a topic assigned by the teacher and her collaboratos, or an oral test on the course contents. In case of no project/oral, the final mark is the one obtained in the written test clipped to maximum 27/30. The project can add up to 4 points, the oral can add up to 2 points. Live question sessions (optional) are organized during the lessons, to facilitate self-assessment, contributing to up to 4 extra points; quizzes can be done only in presence.
The instructor reserves the right to require an oral examination.
Details on examination phases and modalities are in the table below.
Type of assessment
Description
Dublin descriptor
Written test (mandatory)
Numerical exercises.
Open-ended questions.
Close form question (true/false).
1, 2, 3, 4, 5
Project (optional)
The project consists in the development of a software prototype of a localization/mobility system in Matlab and assessment by simulations. The topic is assigned during the lab lessons and remains valid for the whole academic year (till Jan-Feb session included). It can be developed individually or in group.
Few days before the discussion of the projects with the teachers, students are required to submit their presentation file and source codes.
Software tools for project development will be acquired during the laboratory hands-on activities: students will have the opportunity to work on practical use-cases, practicing with real data acquired during experimental tests and/or projects in collaboration with companies of the sector.
2, 3, 4, 5
Quiz (optional)
Live question sessions are organized during the term for self-assessment. Quiz tests can be done only in presence.
1, 2
Oral test (optional)
Oral test on course contents.
3, 4, 5

Course website on the webeep platform of Politecnico di Milano: lecture notes; laboratory notes and codes; seminars by experts on positioning, navigation and smart mobility, from university/industry (approx. 2-3 seminars per year, since 2010-2011); projects by former students; examination papers; further material and information. www.polimi.it
A. Küpper, Location-based Services. Fundamentals and Operation, Editore: Wiley, Anno edizione: 2005
Note: Part 1 of the course - Fundamentals of localization and navigation
A. Bensky, Wireless Positioning Technologies and Applications, Editore: Artech House, Anno edizione: 2008
Note: Part 2 of the course - Terrestrial radio positioning
A. C. García, S. Maier, A. Phillips, Location-Based Services in Cellular Networks from GSM to 5G NR, Editore: Artech House, Anno edizione: 2020
Note: Part 2 of the course - Terrestrial radio positioning
R. Prasad e M. Ruggieri, Applied Satellite Navigation Using GPS, GALILEO, and Augmentation Systems, Editore: Artech House, Anno edizione: 2005
Note: Part 2 of the course - Satellite radio positioning
P. D. Groves, Principles of GNSS, Inertial, and Multisensor Integrated Navigation Systems, Editore: Artech House, Anno edizione: 2007
Note: Part 2 of the course - Satellite radio positioning
E. D. Kaplan e C. Hegarty, Understanding GPS: Principles and Applications, Editore: Artech House, Anno edizione: 2005
Note: Part 2 of the course - Satellite radio positioning
B. Dalla Chiara, ITS nei trasporti stradali, Editore: EGAF, Anno edizione: 2013
Note: Part 3 of the course - Intelligent transportation systems
C. F. Daganzo, Fundamentals of Transportation and Traffic Operations, Editore: Pergamon-Elsevier, Anno edizione: 1997
Note: Part 3 of the course - Intelligent transportation systems
Claudia Campolo, Antonella Molinaro, Riccardo Scopigno, Vehicular ad hoc Networks - Standards, Solutions, and Research, Editore: Springer, Anno edizione: 2015, ISBN: 978-3-319-15497-8
Note: Part 3 of the course - Cooperative ITS
Markus Dillinger, Mikael Fallgren, Toktam Mahmoodi, Tommy Svensson, Cellular V2X for Connected Automated Driving, Editore: Wiley, Anno edizione: 2021, ISBN: 978-1119692645
Note: Part 3 of the course - Cooperative ITS

The main objective of the course is to provide the students with the necessary tools for the design and assessment of machine components, both under static and fatigue loading. The basics of mechanical design are illustrated in order to include capability to develop simplified structural models, basic theory of elasticity, design methods for static and fatigue loads (multiaxial and variable amplitude loading) and tools for assessing the influence of notches on static and fatigue strength.

Once the students has passed the exam, the students will:
possess the knowledge of the main tools (analytic approach) and methods for modelling structural behaviour of simple machine components/systems; (DdD1)
understand assumptions and limitations of the beforementioned tools and methods; (DdD1)
perform design and assessment of machine components/systems subjected to static and fatigue loading; (DdD2)
analyse and critically discuss the results. (DdD2)
Thanks to the practices that are focussed on case studies (basic optimization technique, bolted and welding joints, basic of gears, pressure vessels, power transmission shafts, static design of a prothesis) the students will improve the skills necessary for practical design applications. Moreover, the theoretical concepts and the case studies will provide together a more general capability to approach an engineering solution of simple problems related to structural design in several fields. These competences are therefore of benefit for a wide range of study courses.

The course is organized in lectures and tutorials. Theoretical concepts will be applied during application in order to show an engineering approach to the design and assessment of actual and relevant components.
Internal forces: definition of slender members, definition of internal forces and equations of equilibrium, frames;
Axial and bending problem in beams: hypotheses and basic assumptions for the Euler-Bernoulli beam; state of stress due to normal force and bending moment;
Shear in beams: relationship between bending moment and shear force; shear stress distribution in case of solid section; torsion in beam: distribution of torsional stresses for a circular solid beam;
State of stress: definition of equilibrium for deformable bodies; Cauchy tetrahedron; principal stresses and directions; stress invariants; hydrostatic stress tensor and stress deviator tensor; Mohr’s circles for plane stress; three-dimensional description of Mohr’s circles;
Material behaviour: tensile test devices and specimens; engineering stress-strain curve; true stress-strain curve; ductile and brittle failure mechanisms; ideal material models;
Influence of notch for ductile and brittle materials: phenomenological aspects; definition of Kt and Ks parameters.
Stress-strain relations: general expression of the elastic tensor; elastic tensor for isotropic materials; derivation of the generalised Hooke’s law; stress-strain relationships for plane stress and plane strain.
Failure criteria for isotropic materials: Galileo-Leibniz-Rankine-Navier criterion for brittle materials; Guest-Saint Venant-Tresca criterion for ductile materials; Huber-Hencky-von Mises criterion for ductile materials; general considerations about the definition of the safety factor;
Fatigue: fatigue phenomena; fatigue testing; definition of fatigue cycles; Wöhler diagram; finite and infinite life regime; relationship between fatigue limit and tensile strength; influence of mean stress with Haigh diagrams; notch effect on fatigue; influence of surface finishing; size effect and influence of stress gradient; damage accumulation with Miner-Palmgren rule; Gough-Pollard and Sines criteria for multi-axial loading condition.

Prova scritta obbligatoria, senza prove in itinere
Prova orale facoltativa (a scelta dello studente)
The exam is based on a written test followed by a voluntary oral one. Written exam is compulsory and cover all the theoretical and applicative topics of the course. The written test will include the solution of numerical exercises, aimed at assessing the capability of application of the design tools to practical cases and questions aimed at assessing the students’ capability of application of the design tools to simple practical cases regarding machine components and systems and/or to test students’ knowledge and understanding of the theoretical topics included in the program. Oral exam is voluntary and possible only in case written exam has been positively passed; oral exam can modify the score of +- 3 points and have to be carried out in the same exam session of the written one. The oral test will prove also the capability of critically discuss the design tools and their limits and or applicability conditions. Practical exercises can be also requested during oral exam.

Lecture notes by the teacher (english) Beep
N. E. Dowling, Mechanical Behavior of Materials, Global Edition 5th Edition, Editore: Person, Anno edizione: 2019
AA. VV, Costruzione di Macchine, Editore: McGraw Hill
Note: (italian language)

The course will offer students basic and advanced knowledge in robotics, for design and programming in medical applications.
The course has the following objectives:
To make students familiar with kinematic structures of medical robots
To provide students with basics of medical robotics, in terms of general hardware (actuators and sensors) and software (planning and control)
To give students the knowledge on the basic design requirements for a robot to be used for medical applications
To make students able to design and test novel robotic structures for a medical-related application.
Each topic is treated both theoretically and practically through a project/ discussion work.

Students will gain knowledge and understanding of (Dublin Descriptor 1, DD1):
Elements included in a medical robot design and its control schema, including autonomous reasoning
Design requirements of a robot to be used in the medical sector
Design patient-centric control and planning with emphasis on safety, compliance, and delicate physical interaction.
Sensory information processing and context awareness, integrating medical perception (intra-operative imaging, tracking, force sensing) into closed-loop control.
Outcome measures to evaluate robotics in medical settings.
Students will acquire the capability to (DD2):
Design and implement novel solutions for medical robot
Model and simulate the robot behavior
Students will also develop skills relevant to (DD3, DD4 and DD5):
Apply an interdisciplinary approach for healthcare related applications
Benchmark robotics system in healthcare (accuracy, sterility, usability, risk).

Frontal lectures
History of medical robotics and taxonomy of medical robots
Design issues for robotics in healthcare:
Clinical constraints and metrics for validation
Sensors and actuators for medical applications,
Kinematic structures of robots in healthcare
Registration robot-to-imaging
Minimally invasive surgery: vision and control
Situation awareness in surgical robotics
Detection and classification of surgical tools and anatomical structures
Visual servoing
Teleoperation (scaling, clutching, tremor filtering, passivity and stability)
Autonomy in surgical applications
Interventional and continuum robots
Continuum robots modelling
Motion planning (trajectories for surgical)
Sensing and actuation for diagnosis/ therapies – soft catheters
Navigation and control with friction/ hysteresis compensation
Human robot interaction
Augmented reality
Haptics and force feedback
Virtual constraints
Humanoids for rehabilitation and Telepresence for remote diagnosis (examples)
Regulatory aspects, safety, ethical issues and certification
Hands-on / practice
Simulation environment for medical robotic applications
Computer vision tools for endoscopic image processing

Prova scritta condizionata (a scelta del docente), senza prove in itinere
Prova orale facoltativa (a scelta dello studente)
Students will be assessed on the work produced during each stage of the practical exercises. Their knowledge of the topics covered in the lectures and included in the course bibliography will also be evaluated (DD1). Assessment will take into account students’ participation and interaction during the project activities (DD4), the outcomes of the design exercises (DD2 and DD3), their ability to work both independently and as part of a team (DD3 and DD4), and the quality of the representation and presentation of their design work (DD4).
The course will conclude at the end of the semester with the final evaluation (mandatory written test and compulsory interview) and the assignment of the final grade to all students. The final examination is intended to enable students to:
demonstrate that they have acquired a design approach that, through knowledge of technologies, enables them to identify technically feasible pathways for the development of medical robots (DD1 and DD2);
demonstrate the ability to respond effectively and promptly to design problem-solving challenges (DD2 and DD3);
demonstrate the ability to effectively solve problems related to the control of medical robots (DD2 and DD3);
communicate and justify design choices, technical solutions, and engineering decisions using appropriate scientific and technical terminology during the oral examination (DD4).

Path Planning: Recap from differential geometry and numerical analysis, including properties of parametric 2D/3D curves, curvature and torsion, interpolation/approximation. Overview of vehicle kinematic and dynamic constraints and their connection to path planning. Basic path generation for connecting waypoints: Dubins paths, Reeds-Shepp car, splines, Pythagorean Hodographs, spirals. Obstacle representation. Roadmap methods for generating waypoints: Voronoi diagrams, probabilistic roadmaps, RRTs. Graph-search algorithms for dynamically computing the optimal sequence of waypoints. Optimal control and optimization approaches. Potential fields.AI planning: Introduction to Markov Decision Processes (MDPs) and solutions based on dynamic programming and reinforcement learning. Deliberative vs reactive behaviour. Fundamental automated planning methods and algorithms (state machines, STRIPS). Hierarchical task networks (HTNs). Temporal models. The course will also include one lecture with an introduction to the Robot Operating System (ROS).

The goal of the course is to provide students with the fundamental methodologies for modelling, analysing, and designing mobile radio networks. Students will learn how radio resources are shared and reused, how mobility is managed, and how coverage, capacity and traffic requirements drive network design choices. In particular, the course covers the architectures, radio interfaces, protocols, and management mechanisms of cellular systems, from 2G to 5G.

Dublin Descriptor
Expected learning outcomes
1 - Knowledge and understanding
Students will learn how to:

• Define mathematical models for the analysis of mobile radio networks
• Analyse complex technologies related to mobile radio networks
• Identify system components and their relations
  2 - Applying knowledge and understanding
  Students will be able to:
• Dimension the components of a mobile radio network to meet traffic requirements
• Evaluate the performance of mobile radio networks across different scenarios
• Apply the mathematical models learned
  3 - Making judgements
  Students will be able to:
• Evaluate the trade-offs that govern the design of mobile radio networks and justify design choices accordingly
• Identify how the fundamental system limitations impact the design of the network
• Recognise the design space and its degrees of freedom that can be exploited to define new technologies
  4 - Communication
  Students will be able to:
• Describe complex network technologies at different levels of detail, also comparing them with respect to specific aspects
• Report on the analysis and results of an advanced topic in a technical document (optional)
  Learning skills
  Students will be able to study autonomously new mobile radio technologies by applying and extending the methods learned during the course

1. Mobile Network Basics
Multiple-access techniques in cellular radio networks
Cellular coverage and resource reuse
Network architecture and functional elements
Mobility management: cell selection, location management, and handover
2. Capacity and radio planning
Mathematical models and algorithms for coverage optimisation
Mathematical models and algorithms for static frequency assignment
Dynamic channel allocation techniques
3. Performance analysis
Models for the capacity of CDMA systems
Traffic models for the performance evaluation at session level
Models for the dimensioning of cells and location areas
4. 2G systems (GSM and GPRS/EDGE)
Architectures, interfaces, physical and logical channels
Signalling architecture, signalling protocols, network databases, call management procedures, mobility management procedures.
GPRS/EDGE: radio interface, IP network, interconnection nodes, mobility management, protocol architecture
5. 3G systems (UMTS and HSPA)
Radio access network
Core network
Radio resource management
High-speed packet data services
6. 4G systems (LTE and EPC)
System architecture
Radio Interface
Core Network
7. 5G systems
System architecture
New Radio interface
Network slicing

Prova scritta obbligatoria, senza prove in itinere
Progetto/i - Elaborato/i facoltativo (a scelta dello studente), di gruppo, senza revisioni periodiche, senza presentazione/discussione finale
The assessment will be based on a written exam at the end of the course and optional project activities.
The written exam is worth up to 28 points and comprises numerical exercises, closed-ended questions, and open-ended questions covering all course topics. Optional project activities may contribute up to 4 additional points. The final score is the sum of all points obtained.

Slides and recommended readings are provided by the lecturers through Webeep https://webeep.polimi.it/

An overview over different types of manipulators. Kinematics: description of position and orientation of rigid bodies, the Denavit-Hartenberg convention, forward and inverse kinematics, the velocity transformations, singularities, kinematics for vehicles. Dynamics: dynamics of rigid bodies, reference frames in relative motion, equations of motion for manipulators and vehicles in closed and recursive form, elasticity. Motion Planning: point-to-point motions, interpolation and path primitives, localization of robots, mapping a robot environment. Control: feedback linearization, passivity based controllers, position and force control.

Physical processes involving fluid flow, heat transfer and thermal/mechanical power conversion are at the heart of numerous engineering systems, whose performance crucially depends on control. The main goal of the course is to provide the methodological foundations for the control-oriented dynamic modelling of such systems, enabling the students to understand what are the control-relevant phenomena, how to model them and how to use these models to design effective control strategies. Particular emphasis is put on the concept that the achievable control performance is determined by the dynamic physical behaviour of the process and on establishing links between physical process behaviour and control-relevant aspects.
The concepts are applied to a number of examples in the field of process control and thermal power generation, thus also providing the students an overview of typical control problems in this field and on how they can be solved effectively once the dynamic process behaviour has been clearly analyzed. The course mostly focuses on cases that can be analyzed with simple, closed-form analytic models, but also briefly discusses the role of numerical modelling and simulation for more complex cases.

Knowledge and understanding (Dublin descriptor DD1). The student:
knows the basic conservation equations governing thermo-mechanical-hydraulic processes, both lumped-parameter (0D) and distributed-parameters (1D)
knows the constitutive equations of the models of specific phenomena (such as pressure losses or heat transfer) as well as of specific machinery (such as valves, turbines, compressors, etc.)
understands the time scale of different dynamic phenomena in these processes
is aware of how classical control problems are solved in the field of fluid processing and thermal power generation
understands the role of simplified analytical control-oriented models and of more detailed numerical simulation models for the design of control systems
understands how the mechanical design parameters and the operating point of such systems influence their controllability and how it may be possible to improve the control performance also by changing these parameter (process-control co-design)
understands what are the main control problems in a number of applications in the field of fluid processing and thermal power generation and which model-based control strategies can be used to solve them
Capability to apply knowlegde and understanding (Dublin descriptor DD2). The student is able to
estimate the time scale of different dynamic phenomena in thermo-hydraulic-mechanical processes, so as to select the ones which are relevant for control while neglecting those which are not
derive control-oriented dynamic models of thermo-hydraulic-mechanical processes
analyze these models and simplify them as much as possible to gain insight on the relationship between the mechanical design and operating parameters, the control design, and the achievable control performance
use the information gathered from the analyis of the system dynamics to determine the structure/architecture of effective control systems
use appropriate numerical methods to extract control-relevant information from detailed numerical simulation models of the process
interact with specialists in the area of thermo-hydraulic-mechanical process design in multi-disciplinary teams with the ultimate goal of designing better-controlled innovative systems
Judgement and autonomy (Dublin description DD3): The student is able to analyze thermo-hydraulic system dynamics, making educated guesses of unknow data and identifying critical missing data about the process.
Communication (Dublin descriptor DD4). The student is able to effectively present the results of a case study where the concepts, models and methods learned in class were applied to the design of an actual control system, whose performance can be analyzed by means of simulations.

Part 1: Introduction and fundamental equations
Role of modelling for the design of control systems
Detailed models for numerical simulation vs. simplified analytical models for control design
Mass, energy and momentum balance equations for 0D systems
Mass, energy and momentum balance equations for 1D systems
Estimation of time scales of different phenomena in 1D systems
Equations of state and thermodynamic properties of working fluids
Part 2: Modelling of components of thermo-hydraulic and power production processes
Control valves and piping equipment
Turbomachinery: pumps, compressors, turbines
Simplified models of combustion phenomena
Single-phase heat exchangers
Two-phase heat exchangers and boilers
Electrical power generation and transmission equipment
Part 3: Analysis and design of control systems
Control of simple hydraulic circuits
Power/frequency control in hydro power plants
Temperature control in single-phase heat exchangers
Control pressure, level and load in simple steam generators
Control problems in coal-fired and combined-cycle power plants
Numerical methods to extract control-relevant information from numerical models
Applications in the control of innovative power generation systems

Prova scritta obbligatoria, senza prove in itinere
Prova orale facoltativa (a scelta dello studente) o condizionata (a scelta del docente)
Progetto/i - Elaborato/i obbligatorio, di gruppo, senza revisioni periodiche, con presentazione/discussione finale
The final learing assessment is by written exam, with optional oral discussion to change the final mark up to +/- 3 points. During the exam, the student will be asked to
Write down appropriate conservation laws and consitutive equations for all the system components described in the course (DD1)
Evaluate the time scale of different physical phenomena in a given process and, based on that, decide what are the most appropriate modelling assumptions for a control-oriented model (DD2)
Derive simplified models of the fundamental dynamics of a given process, that can be used for control design, and use them to derive a control strategy and possibly to tune the controller parameters (DD2)
Describe and discuss the control-relevant dynamic phenomena, the control problems and the control strategies of the systems discussed during the course (DD1, DD4)
The student will also be asked to hand in a home assignment, where he/she will be asked to design and test the controller of a specific process (DD2, DD4). A detailed simulation model of the process will be provided, as well as an open-source environment to run it.

Lecture notes
G. Quazza, Controllo dei processi, Editore: Clup, Anno edizione: 1976
R. Dolezal, L. Varcop, Process Dynamics - Automatic Control of Steam Generation Plant, Editore: Elsevier, Anno edizione: 1970
C. Maffezzoni, Dinamica dei generatori di vapore, Editore: Masson, Anno edizione: 1989
C. Maffezzoni, Controllo dei generatori di vapore, Editore: Masson, Anno edizione: 1990
F. Saccomanno, Electric Power Systems: Analysis and Control, Editore: Wiley Interscience, Anno edizione: 2003
S.G. Dukelow, The Control of Boilers, Editore: ISA, Anno edizione: 1991

Computer systems are increasingly intelligent, distributed, and interconnected. This trend naturally leads to the development of systems composed of autonomous intelligent decision-making entities, called autonomous agents, that interact with each other in complex environments. The advent of AI agents based on Large Language Models is just the last step along this path. Agent-based systems are an abstraction of specific computing systems deployed in several applications, including electronic commerce, control of industrial processes, logistics, ambient intelligence, web services, robotics, space systems, and modeling of complex systems.
This course aims to present general techniques for developing multiagent systems, independently of the implementation technology and of applicative domains. In particular, the course, after shortly presenting methods for developing single agents able to make rational decisions in situations affected by uncertainty, will focus on methods for developing systems composed of multiple agents, with special emphasis on the interaction mechanisms between the agents. Moreover, some real-world applications of agent systems will be discussed. At the end of the course, students will acquire the ability to design and develop distributed systems based on the agent paradigm.
This course extends the (mostly) single-agent point of view taken in other courses (including, for example, Foundations of Artificial Intelligence and Machine Learning) to consider the design and the development of collections of intelligent agents, which are currently envisaged by several recent developments, including Agentic AI, and initiatives, including that on Cooperative Artificial Intelligence.

Dublin descriptors
Expected learning outcomes
Knowledge and understanding
Students will learn:
the models that represent distributed systems as collections of computational systems, called autonomous agents, that interact together,
the models that represent individual autonomous agents as rational utility maximizers,
some significant types of interaction mechanisms between multiple autonomous agents (coalition formation, planning, distributed constraint optimization, learning,...),
some of the relevant algorithms available to implement the interaction mechanisms and their properties (range of application, optimality guarantees,...).
Applying knowledge and understanding
Given application cases that involve multiple decision-makers, students will be able to:
develop agent-based models and systems that are based on the identification of individual decision-making entities (autonomous agents) and on the identification of the interaction mechanisms between them,
analyze and understand existing multiagent systems.
Lifelong learning skills
Students will be able to understand and critically evaluate the modeling based on multiagent systems composed of multiple decision-makers.
Students will be able to understand, learn, and develop new interaction mechanisms for autonomous agents.
Students will be able to apply acquired knowledge when designing complex distributed computational systems.

1. Introduction to the concepts of autonomous agents and multiagent systems.
2. Autonomous agents as rational decision-makers: architecture for intelligent agents, Markov decision processes.
3. Interactions between self-interested agents: short introduction to game theory, coalition formation.
4. Interactions between cooperative agents: multiagent planning, distributed constraint optimization.
5. Multiagent learning: multiagent Markov decision processes and stochastic games, multiagent reinforcement learning, evolutionary game theory.
6. Examples of real-world applications of agent-based systems, including Agentic AI applications.

Prova scritta obbligatoria, senza prove in itinere
Prova orale condizionata (a scelta del docente)
The students' assessment is based on a closed-book written test at the end of the course, consisting of exercises and questions on all the topics of the course. The test will typically last 75 minutes. The test assigns a maximum of 31 points (30 cum laude is assigned when the total score is strictly larger than 30 points). Students can take the written test at any exam session during the year. The instructor can summon students who passed the written test for an additional (in-person) oral exam that will cover the entire course program. Students cannot request to do oral exams.
Type of assessment
Description
Dublin descriptor
Written test
Solution of numerical problems
Application of algorithms to given problem instances.
Exercises focusing on design aspects
Designing models of autonomous agents and of their interaction mechanisms for simple problems
Theoretical questions on all the course topics with open answer
1, 2
1, 2, 5
1, 5

Gerhard Weiss (editor), Multiagent Systems (second edition), Editore: The MIT Press, Anno edizione: 2013, ISBN: 978-0-262-01889-0 https://ebookcentral.proquest.com/lib/polimi/detail.action?docID=3339590
Note: Web site of the book: http://www.the-mas-book.info
Further teaching material could be possibly made available along the course.

Behandelt werden verschiedene Verfahren der robusten und nicht robusten Reglersynthese von Ein- und Mehrgrößensystemen im Frequenzbereich. Insbesondere werden folgende Inhalte behandelt:

• Wiederholung nicht robuste Reglersynthese, Loop-Shaping
• Einschränkungen der erreichbaren Regelgüte
• Unsicherheitsbeschreibungen für SISO- und MIMO-Systeme
• Eigenschaften und Analyse von Mehrgrößensystemen
• Einfache (klassische) Regelungsansätze für Mehrgrößensysteme
• Performance und Robustheit für SISO- und MIMO-Systeme
• Reglersynthese für Mehrgrößensysteme (H2- , H-unendlich- und mu-Synthese)

Design of Experiments
Principal Component Analysis
Multivariate regression methods (MLR,PCR,PLSR)
Strategies for model selection and validation (bias-variance trade-off)
Features and variables selection
Classification methods (Machine learning)
Tree-based methods
ANN/CNN
Time series analysis
Prediction Error Methods for the Identification of dynamical systems
Kalman filters
Metamodelling & hybrid modelling
Independent Component Analysis
PARAFAC, multiblock (sensor fusion) and IDLE modelling

The course develops knowledge and skills for advanced modelling, management and control of natural resources systems. The emphasis will be on the operational aspects of natural resources modeling and management, with focus on water resources systems and real world case studies. Topics include: modeling for management, uncertainty and sensitivity analysis, stochastic and robust optimal control, approximate methods, real time control, and complexity reduction methods. The course is aimed at graduate students preparing to work in environmental and water resources engineering field.
Course delivery and contents: Modelling for management, Optimal planning, Approximate Dynamic Programming and Reinforcement Learning; direct use of exogenous information; Real time control: deterministic and stochastic Model Predictive Control; deterministic simulation; MonteCarlo simulation. Dimensionality and complexity: non dynamic emulation modeling (iterative Response Surface); model reduction/dynamic emulation modeling; objective reduction; from multi to many objective; multidimensional visualization tools.

In the past decades a growing interest has been dedicated to networked systems and large-scale plants where parts of the system, e.g., sensors, actuators, are not collocated and are possibly distributed over a large geographic area. These classes of systems include, e.g., large chemical plants, power distribution networks, industrial factories, water distribution networks, and multiple agents cooperating with each other through suitable coordination schemes (e.g., unmanned aerial and ground vehicles and teams of robots).
The control of these systems may require communication through a shared transmission network and/or to resort to complex (e.g. decentralized, distributed, or hierarchical) schemes.
However, one the one hand, communication through a shared transmission network involves a number of issues, e.g., bandwidth limitation, delays, packet dropouts, which can compromise the stability properties of the network. On the other hand, distributed and decentralized control structures entail structural constraints and limitations, and call for non-standard analysis and control design tools.
The course covers the necessary mathematical and technical tools for the analysis and the design of possibly complex control systems subject to communication and structural issues.

Lectures and exercise sessions will allow students to:
Understand the challenges, opportunities, and issues related to networked control systems.
Understand the possible sources of communication problems, including bandwidth limitations, delays, and packet dropouts.
Learn how to devise mathematical models where communication issues are formally described, and perform stability analysis of networked control systems, i.e., where communication takes place through a serial communication bus.
Understand the theoretical limitations (e.g., decentralized fixed modes) related to decentralized and distributed control structures, and learn how to design decentralized and distributed regulators with linear matrix inequalities (LMIs).
The hands-on activities allow students to l earn how to use Matlab and Yalmip to
analyze the properties of dynamical systems, e.g., the stability of linear parameter-varying systems and Networked Control Systems using LMIs
design state-feedback centralized, decentralized, and distributed controllers using LMIs based on different design criteria, e.g., H2 and Hinfinity.
With respect to the study programme, the course will allow students to reach the following generic learning outcomes:
[DdD1] Knowledge and understanding
[DdD2] Capability to apply knowledge and understanding
[DdD3] Making judgements
[DdD4] Communication skills
[DdD5] Learning skills

The course will cover the following topics:
INTRODUCTION: Introduction on networked control systems, control architectures for large-scale and networked systems, and application areas. Issues and challenges of networked control systems.
PRELIMINARIES: Basic and more advanced concepts in control theory (with focus on linear systems) will be recalled and covered, in order to provide consistent background to the students, e.g.
Stability of continuous-time and discrete-time systems;
Controllability and observability;
Basics on linear matrix inequalities in control systems analysis and design.
CONTROL WITH COMMUNICATION ISSUES.
Communication issues in networked control systems. Control networks, packet transmission, and transmission protocols.
Control with bandwidth limitations: minimum channel capacity requirements.
Network-induced delays and packet dropouts. Stability analysis in case of network-induced delays and packet dropouts.
DECENTRALIZED AND DISTRIBUTED CONTROL STRUCTURES.
Definition of decentralized fixed modes and stabilization with decentralized regulators.
Design of decentralized and distributed controllers using linear matrix inequalities for both continuous and discrete-time systems.
The course consists of:
Theoretical lectures (about 30 hours).
Numerical exercise lectures (about 6 hours).
Computer laboratory lectures (about 8 hours), where the described analysis and design methods will be implemented using dedicated computer tools and programs (e.g., MATLAB and YALMIP).
Project laboratory lectures (about 4 hours): the teacher will assign a project to each group of 1-4 students regarding the design of decentralized and distributed controllers for a selected benchmark case study. The project discussion will be matter of evaluation during the oral exam.

Prova scritta obbligatoria, senza prove in itinere
Prova orale obbligatoria
Progetto/i - Elaborato/i obbligatorio, di gruppo, senza revisioni periodiche, con presentazione/discussione finale
The Exam consists of two parts aiming to evaluate, not only the students’ knowledge and understanding of the course topics, but also their ability to apply the acquired concepts and theoretical/practical tools.
Regarding “Control with communication issues”, a written test must be taken, consisting of numerical exercises and theoretical questions. The numerical exercises (DdD2 - capability to apply knowledge and undestanding) will assess the capability of students to: (a) devise mathematical models where communication issues are formally described; (b) perform stability analyses of networked control systems, possibly in simplified cases.
Open or multiple-choice questions will be used to assess the knowledge of students (DdD1 - Knowledge and understanding) of the following topics.
Communication issues in networked control systems.
Mathematical modelling and stability analysis of networked control systems under communication issues.
Regarding “Decentralized and distributed control structures”, a project work must be completed (DdD2 - capability to apply knowledge and undestanding; DdD3 - Making judgements; DdD5 Learning skills) and presented (DdD4 - Communication skills) in a short oral discussion, where the comprehension of the main theoretical concepts will also be examined (DdD1 - knowledge and understanding). The students’ ability to apply the following tools and concepts will be evaluated.
Centralized, decentralized and distributed fixed mode analysis.
Design of decentralized and distributed controllers for large-scale systems.

Slides, lecture notes, and computer programs will be made available to students on the BeeP Portal. https://beep.metid.polimi.it/
R. J. Baillieul, P. J. Antsaklis, Control and communication challenges in networked real-time systems, Editore: Proceedings of the IEEE, 95 (1), pp. 9-28.
J. R. Moyne and D. M. Tilbury, The emergence of industrial control networks for manufacturing control, diagnostics, and safety data, Editore: Proceedings of the IEEE, 95 (1), pp. 29-47.
R. J. Baillieul, Feedback coding for information-based control: operating near the data-rate limit, Editore: Proceedings of the IEEE Conference on Decision and Control, 2002, pp. 3229-3236.
W. Zhang, M. S. Branicky, S. M. Phillips, Stability of networked control systems., Editore: IEEE Control Systems Magazine, 2001, pp. 84-99.
J. P. Hespanha, P. Naghshtabrizi, Y. Xu, A survey of recent results in networked control systems., Editore: Proceedings of the IEEE, 95 (1), pp. 138-162.
J. Lunze, Feedback Control of Large-Scale Systems, Editore: Prentice Hall International Series in Systems and Control Engineering, Anno edizione: 1992
Zecevic, D. D. Siljak, Control of complex systems. Structural constraints and uncertainty, Editore: Springer, Anno edizione: 2010

Nonlinear systems exhibit richer phenomena than their linear counterparts, at the price of increased complexity.
To navigate this complexity, the course first presents the main tools to analyze dynamical properties of nonlinear systems, among which Lyapunov methods and changes of variable play a prominent role. Through these tools, then, the course illustrates well-established techniques to design controllers for relevant classes of nonlinear systems, as described below. Mechanical and robotic systems, oscillators and electrical circuits are used to exemplify these techniques.
The course is methodological and aims at providing students with a toolkit of techniques for nonlinear (control) systems with an eye towards computational implementations of some of these techniques. This toolkit and the understanding gained in the course should enable students to deal with the instances of nonlinear (control) systems they will encounter and to autonomously delve into more advanced topics within the broad area of nonlinear control.

After the lectures and exercise sessions of this course, the student

• knows the fundamental tools for the analysis of dynamical properties of nonlinear systems (DD1);
• knows some of the most used techniques for control design of nonlinear systems (DD1);
• is able to explain these techniques (DD5), rigorously justify the main steps in their derivation (DD2), and suitably apply them according to the context (DD2).
  After the computer sessions of this course, the student
• is able to numerically implement control design algorithms (DD2);
• is familiar with some computational tools for the analysis and design of classes of nonlinear systems (DD2).
  DD1-DD5 refer to Dublin descriptors 1-5.

The course covers some selected topics within control design of nonlinear systems.

• Review on autonomous nonlinear systems.
• Passivity.
• Input/output models, L2 gain and its application to antiwindup design.
• Special nonlinear forms.
• Feedback linearization.
• State feedback stabilization for nonlinear systems.
  The course comprises
• lectures, where theoretical notions and methods are presented;
• exercise sessions, where the theoretical notions and methods are illustrated on numerical examples of nonlinear systems;
• computer sessions, where more realistic examples of nonlinear systems and computational tools (such as linear matrix inequalities and sum-of-squares) are illustrated.

Prova scritta obbligatoria, senza prove in itinere
Prova orale condizionata (a scelta del docente)
The final assessment is a written exam consisting of

• exercises, where the students will show their knowledge on analysis and design methodologies for nonlinear control systems (DD1) as well as their capability to apply these methodologies to some numerical problems (DD2);
• theoretical questions, where the students will demonstrate their ability in explaining how to address (DD4) and solve (DD2) an analysis/design problem for a nonlinear control system, according to a given methodology.
  An oral exam is not part of the assessment, unless the lecturer deems one necessary for the evaluation after the written exam.
  DD1-DD5 refer to Dublin descriptors 1-5.

H. K. Khalil, Nonlinear Control, Editore: Pearson, Anno edizione: 2015, ISBN: 978-0133499261
C. M. Kellett and P. Braun, Introduction to Nonlinear Control: Stability, Control Design, Estimation, Editore: Princeton University Press, Anno edizione: 2023, ISBN: 978-0691240480
H. K. Khalil, Nonlinear Systems Third Edition, Editore: Prentice Hall, Anno edizione: 2002, ISBN: 978-0130673893
M. Vidyasagar, Nonlinear systems analysis, Editore: SIAM, Anno edizione: 2002, ISBN: 978-0-89871-526-2
S. Boyd, L. El Ghaoui, E. Feron and V. Balakrishnan, Linear Matrix Inequalities in System and Control Theory, Editore: SIAM, Anno edizione: 1994, ISBN: 978-0-89871-485-2

Methods for analysis and design of nonlinear systems, with an emphasis on nonlinear control systems. The course includes: 1) Mathematical models of nonlinear systems, and fundamental differences between the behavior of linear and nonlinear systems. Equilibrium points, limit cycles and general invariant sets. 2) Phase plane analysis, Lyapunov stability, Input-to-state stability, Input-Output stability, and Passivity analysis. 3) Nonlinear control design, including Lyapunov-based control, Energy-based control, Cascaded control, Passivity-based control, Input-Output linearization, Adaptive Control and Backstepping.

Il corso si propone di presentare i principali metodi di ottimizzazione non-lineare, continua e discreta. Contenuti: Condizioni di ottimalita' per problemi non vincolati e vincolati, funzioni Lagrangiane e dualita'; metodi di tipo gradiente, di tipo Newton e a passo ristretto; programmazione quadratica ricorsiva e metodi con funzioni di penalita'. Complementi di ottimizzazione discreta: rilassamenti, metodi di branch-and-bound e poliedrali, euristiche.

This course introduces basic concepts and methods of Numerical Analysis with the aim of providing reliable and accurate approximations to problems recurrent in the engineering practice, described via linear and nonlinear mathematical models. The different numerical methods are theoretically derived with particular attention to key concepts like convergence, consistency, and stability.
The course is organized in lectures and computer labs. Laboratory sessions are meant to provide practical feedback to the theoretical knowledge with the implementation of the algorithms and methods presented during the lectures. All computations are carried out using the Matlab scientific software environment.

Knowledge and understanding:
‐ the basic concepts and the possible errors due to the floating point arithmetics;

• the main numerical methods to approximate data and functions, solve linear and nonlinear equations, derivatives and integrals, and ordinary differential equations;
• the main properties of the considered approximation schemes;
• essential use of the MATLAB language.
  Applying knowledge and understanding:
• apply the learned numerical schemes implementing computer codes in the MATLAB language;
  ‐ using appropriate numerical methods to solve some problems arising from engineering applications.
  Making Judgement:
• be able to select the most appropriate tool for the problem at hand;
• be able to critically assess the quality of the solution and the properties of the adopted numerical method.
  Communication skills:
• be able to interact with other students and teachers during exercise sessions and using the WeBeep forum.

• Floating-point arithmetics: different sources of the computational error; absolute vs relative errors; the floating point representation of real numbers; the roundoff unit; the machine epsilon; floating-point operations; over- and under-flow; numerical cancellation;
• Numerical approximation of nonlinear equations: the bisection and the Newton methods; the fixed-point iteration; convergence analysis (global and local results); order of convergence; stopping criteria and corresponding reliability; generalization to the system of nonlinear equations (hints);
• Numerical methods for continuuous optimization: Free and constrained optimization; Line-search; Newton and quasi-Newton descent directions; Gradient and Conjugate Gradient; Trust region Methods; Constrained optization;
• Numerical approximation of systems of linear equations: direct methods (Gaussian elimination method; LU and Cholesky factorizations; pivoting; sparse systems: Thomas algorithm for tridiagonal systems); iterative methods (the stationary and the dynamic Richardson scheme; Jacobi, Gauss-Seidel, gradient, conjugate gradient methods (hints); choice of the preconditioner; stopping criteria and corresponding reliability); accuracy and stability of the approximation; the condition number of a matrix; over- and under-determined systems: the singular value decomposition (hints);
• Numerical approximation of functions and data: Polynomial interpolation (Lagrange form); piecewise interpolation; cubic interpolating splines; least-squares approximation of clouds of data;
• Numerical approximation of derivatives: finite difference schemes of the first and second order; the undetermined coefficient method;
• Numerical approximation of definite integrals: simple and composite formulas; midpoint, trapezoidal, Cavalieri-Simpson quadrature rules; Gaussian formulas; degree of exactness and order of accuracy of a quadrature rule;
• Numerical approximation of ODEs: the Cauchy problem; one-step methods (forward and backward Euler and Crank-Nicolson schemes); consistency, stability, and convergence (hints).

Prova scritta obbligatoria, senza prove in itinere
Prova orale condizionata (a scelta del docente)
Five exam sessions are scheduled on the dates established by the Faculty calendar.
The exam consists of a written test during which it will be required:
To answer theoretical questions on the topics covered during the course, which may deal with the statement and/or the proof of relevant theoretical results; the precise definition of concepts such as convergence, consistency, and stability of a numerical scheme; the deduction of a numerical method;
To program small computer codes based on MATLAB to solve numerical problems of the type seen at the lectures and during exercise sessions.
An oral assessment may follow the written exam if the teacher deems it necessary.
We assess:
1) The capability of the student to apply the knowledge and technique acquired during the course (Dublin Descriptors 1 and 2);
2) The student's ability to evaluate the result's appropriateness and correctness (Dublin Descriptors 2 and 3);
3) The ability to develop simple numerical computer codes autonomously and evaluate their effectiveness for the problem at hand (Descriptors 3 and 5).

Quarteroni, Alfio, Saleri, Fausto, Gervasio, Paola, Scientific Computing with MATLAB and Octave, Editore: Springer, Anno edizione: 2010, ISBN: 978-3-642-12429-7
Quarteroni, Alfio, Sacco, Riccardo, Saleri, Fausto, Numerical Mathematics, Editore: Springer, Anno edizione: 2007, ISBN: 3-540-34658-9

As industrial applications and systems become increasingly complex and demanding, with the presence of multiple interacting variables, nonlinear dynamics, economic objectives, and operational constraints, the knowledge of tools able to tame such a complexity is today a crucial asset in the portfolio of automation and control engineers. The goal of this course is to provide the students with such tools, introducing them to the formulation and solution of numerical optimization problems arising in automation and control engineering in a broader sense. Following a three-pillars approach (theoretical foundations, implementation aspects, and real-world applications), the lectures, exercise sessions, and project work will deliver effective knowledge and skills to formulate and deal with problems such as parameter estimation and model learning, filtering, control design, and computation of optimal trajectories and equilibria for complex, multivariable, constrained nonlinear dynamical systems. At first, these problems will be abstracted to the general form of a nonlinear program (NLP), starting from real-world case studies in fields such as automotive, aerospace, energy systems, robotics, and process control. Then, the main concepts and tools of numerical optimization for NLPs will be treated, with emphasis on algorithm coding skills and computational aspects specific to problems that involve dynamical systems. Finally, the learned skills and developed code will be tested on the motivating real-world applications, both in guided exercise sessions and in a project work which will be part of the course evaluation.

1. Knowledge and understanding (Dublin Descriptor 1 - DD1)
Upon positive completion of the course, the students will have the knowledge to:
recognize estimation, filtering, and optimal control problems for dynamical systems;
recognize the type of optimization problem at hand (convex vs. non-convex, continuous vs. discrete, constrained vs. unconstrained, etc.);
identify and understand the main solution approaches specifically to constrained nonlinear programs;
understand the main advantages and limits of the existing numerical optimization approaches;
understand how the structure arising in nonlinear identification, filtering, and optimal control problems can be exploited to improve the solution efficiency and quality;
find further resources and critically analyze the toolboxes available in the literature;
understand how constrained numerical optimization of dynamical systems can be applied to common problems arising in application domains including automotive, aerospace, energy systems, robotics, and process control.
2. Capability to apply knowledge and understanding (DD2)
After completing the lectures, exercises, and project work, the students will be able to:
analyze a given real-world problem involving estimation, filtering, or optimal control for complex dynamical systems, and translate it into a sensible numerical optimization problem;
devise innovative approaches, based on constrained numerical optimization, to deal with problems in automation and control of dynamical systems in a broader sense, improving over the state-of-the-art;
apply existing numerical optimization tools developed for standard problem classes;
develop own custom code and ad-hoc algorithms to solve constrained nonlinear programs (sequential quadratic programming, constrained Gauss-Newton approach, efficient computation of derivatives and Hessian approximations), exploiting specifically the structure arising in dynamical systems.
3. Autonomy (DD3)
Through the exercises and project work, the students will learn to:
analyze and discuss, autonomously and within a working group, how to best translate a real-world problem into a sensible optimization program to be addressed with numerical solution algorithms;
critically analyze the performance, advantages and limitations of different problem modelling approaches and numerical solution algorithms;
evaluate the goodness of existing or newly developed optimization models and algorithms and the achieved results, considering the problem specifications.
4. Communication skills (DD4)
Through the project work developed in the course, the related report, and the final presentation, the students will learn to communicate in a clear, concise, autonomous, and effective way:
the problem addressed;
the chosen solution method, including: modelling choices, problem abstraction, and numerical solution;
the obtained results.
5. Learning skills (DD5)
Through the lectures, exercises and project work developed in the course, the students will gain the skills to:
learn the features of optimization problems and solution approaches not treated in detail in this course (structured convex programming, mixed-integer programming);
learn and critically analyze, from the viewpoint of system optimization, implemented solutions for automation and control problems not treated in the course.

Part I: Introduction
I.1. Motivation - numerical optimization-based estimation and control.
I.2. Course structure, goals, pre-requisites, project work description, assessment method.
Part II: Background
II.1. Models of dynamical systems and computation of trajectories.
II.2. Notation and background concepts.
Part III: Constrained estimation and control problems - case studies and general formulation
III.1. Nonlinear and constrained system identification problems, automotive case study (identification of model parameters for the lateral dynamics of cars).
III.2. Constrained filtering problems, robotics case study (estimation of the pose of a worker in collaborative robotics).
III.3. Constrained optimal control problems, aerospace/robotics case study (computation of optimal trajectories for unmanned aerial vehicles) and process control case study (computation of optimal steady-state operation of a chemical batch process).
Part IV: Numerical optimization: fundamentals and notions of unconstrained problems
IV.1. Standard form and definitions, convexity and convex problems, classification of optimization problems. Use of widespread packages (cvx)
IV.2. Unconstrained optimization: optimality conditions, line search with Newton-type methods and globalization strategies. Coding of solvers, use of widpread packages (matlab's fminunc)
Part V: Computing derivatives
V.1. Analytical derivation, recursions for dynamical systems.
V.2. Finite differences and imaginary-part trick.
V.3 Automatic differentiation strategies.
Part VI: Constrained numerical optimization for continuous problems
VI.1. Optimality conditions, Lagrange function and Lagrange multipliers.
VI.2. Quadratic Programming and Sequential Quadratic Programming, line search and interior point methods. Coding of solvers for continuous NLPs based on sequential quadratic programming. Use of widespread packages (CasADi, acados)
Part VII: Solution to constrained optimal estimation and control problems
VII.1. Computation of equilibria and orbits exploiting homotopy. Computation of periodic trajectories with spectral approaches.
VII.2. Constrained optimal control with direct methods: single shooting and multiple shooting. Nonlinear path planning. Nonlinear model predictive control: implementation and computational aspects.
VII.3. Constrained nonlinear estimation with simulation error criterion. Moving horizon estimation: implementation and computational aspects.
VII.4. Multi-objective optimization: Pareto front estimation and a-posteriori evaluation

Prova scritta obbligatoria, senza prove in itinere
Progetto/i - Elaborato/i facoltativo (a scelta dello studente), di gruppo, senza revisioni periodiche, con presentazione/discussione finale
The final assessment will consist of:
1) a written exam with questions regarding the theory and implementation of the methods and algorithms treated in the course. The exam will be instrumental to assess the achievement of expected learning outcomes number 1., 2., and 4. (DD 1, 2,4)
2) (not mandatory) a presentation and a report of the project work carried out by the students in small groups throughout the course. The project work will pertain to a realistic estimation and/or control problem involving a dynamical system, chosen by the students under the guidance of the lecturer. The report will include a problem formulation, a modelling part, the formulation of a sensible optimization problem, and its solution with the knowledge and tools learned during the course. The project work and the related report and final presentation will be instrumental to assess the achievement of expected learning outcomes number 2., 3., 4., and 5. (DD 1, 2,4)
For students taking only the written exam, the maximum grade achievable is 26.
For students that carry out both the written exam and the project work, the maximum grade is 30 cum Laude. In this case, the final grade will be a weighted average of those obtained at points 1) and 2) above. 30 cum Laude requires reaching 30 in both the written exam and project grades.
In-course project option: students that opt for an "in-course" project development will have to present twice during the semester their work-in-progress to the class. They will receive 3 bonus points to the project grade, and will not need to present the project in the exams' session, only hand-in the report and code. Only up to 5 groups can take this option per semester.

Papers and lecture notes provided by the lecturer (the material is available on WeBeeP) https://webeep.polimi.it/
Nocedal, J. and Wright, S., Numerical Optimization, Editore: Springer, Anno edizione: 2006, ISBN: 978-0-387-40065-5
Moritz Diehl, Lecture Notes on Numerical Optimization, Leuven-Freiburg 2007-2016 https://www.syscop.de/files/2015ws/numopt/numopt_0.pdf
Stephen Boyd and Lieven Vandenberghe, Convex Optimization, Editore: Cambridge Univ. Press, Anno edizione: 2004 https://web.stanford.edu/~boyd/cvxbook/bv_cvxbook.pdf

The course aims to provide a deep knowledge of modern optical and optoelectronic measurements. It starts with a review of LASER principles and measurements of main optical parameters of LASER light (spectrum, power, coherence, beam profile), to proceed with applications of optical instrumentation and measurements in scientific and technological fields. The class will study measurement methods and electro-optical systems for electronic material and device characterization (e.g., optical spectroscopy and laser interferometry), optical methods and instrumentation for optical communications, and electro-optical instrumentation for automotive and industrial applications, including optical telemeters, interferometers, laser vibrometers, and velocitmeters.

We expect that the student will be able to understand and solve problems related to optical instrumentation and measurements.
In particular, the student will develop the ability to design and use practical optical telemeters, interferometers, and velocimeters, and understand the capabilities of optical laboratory instrumentation (optical power meter, optical spectrum analyzer, wavelength meter). These abilities will be tested with written exercises and tests and eventually an oral interrogation.
At the end of the course and after successfully passing the exam, the student should reach the following learning results, with reference to the Doublin Descriptors:
Knowledge and understanding (Dublin Descriptor 1, DD1)
knows how to analyze block diagrams of optical measurement setups and to understand the choice of different laser sources on the system performance;
knows how to design coherent optical detection systems, evaluating signal and noise performance;
understands how to use an optical telemeter, interferometer, velocimeter, and optical measurement instrumentation (optical spectrum analyzer, optical power-meter, wavelength-meter).
Applying knowledge and understanding (Dublin Descriptor 2, DD2)
can apply the acquired understanding to assess the operation and performance of available optical measurement instrumentation;
can apply the acquired knowledge and skills to design an optoelectronic sensor or an optical interferometer;
can discuss pros and cons, as well as performance trade-offs, among different optical measurement instruments.
Making judgements (Dublin Descriptor 3, DD3)
given a specific problem and project cases in the field of optical measurements, should be able to analyze the problem, highlight the peculiar requirements and characteristics, and compare autonomously different choices in terms of hardware and system performance;
can communicate effectively the results of his activity regarding the study and design of optical instrumentation, and can autonomously take design decisions.
Communication (Dublin Descriptor 4, DD4)
will be able to write reports about the analysis and design of optical instrumentation;
will be able to write comparisons between different lasers and techniques for the optical measurements.
Learning skills (Dublin Descriptor 5, DD5)
will be able to study and understand autonomously, even different instrumentation
from the one studied in the course;
will be able to understand and calculate the limiting performance of different and new optical measurements and measurement systems;
will be able to deepen his knowledge in the studied topics, and to read and understand scientific and technical publications dealing with laser instrumentation and optical measurements.

Review of incoherent/coherent optical sources and lasers, photodetectors (direct and coherent detection), amplitude and frequency noise in optical oscillators.
Laser telemetry: triangulators, Time-Of-Flight (pulsed/sine-mod.), LIDAR, and DIAL.
Measurements on optical sources: emission spectrum, spatial profile and beam divergence of lasers and LEDs.
Optical power and energy measurements (optical power/energy meters). Polarization measurements. Optical Spectrum Analyzers (OSA). Monochromator and wavemeter for the measurement of optical wavelengths. Measurements for optical communications: Polarization Mode Dispersion (PMD); Optical Time Domain Reflectometry (OTDR); eye diagram and BER measurements on the optical signal.
Laser Doppler Velocimetry (LDV) and Particle Image Velocimetry (PIV), optical autovelox (pulsed tele-laser and laser-barrier).
Laser interferometry: Michelson, Mach-Zehnder, Sagnac, Twyman-Green, Double-Beam, Double-Frequency interferometers. Limits due to phase noise and quantum detection in laser interferometry. Electronic speckle‑pattern interferometry. Self-mixing interferometry. Interferometric techniques for industrial measurements: displacement, planarity, angle, and vibration measurements. Laser interferometry for Gravitational-Wave detection.
Measurements of temperature, vibrations, and intrusion, using optical fibers.
ALL THE SLIDES OF THE COURSE AND PREVIOUSLY REGISTERED LECTURES ARE AVAILABLE IN ADVANCE on the OneDrive space of the Course.
Essay optional topics
Stability and active stabilization of laser oscillators: natural and technical fluctuations of amplitude and frequency of the optical field and their suppression using optoelectronic control loops.
Ultra-high-resolution optical measurements: hi-res and Doppler-free laser spectroscopy; atom manipulation (laser cooling and trapping of atoms); optical frequency clocks.

Prova scritta obbligatoria, senza prove in itinere
Prova orale condizionata (a scelta del docente)
The exam is based on a written test about the topics of the course and on an optional oral interrogation, which is mandatory when requested by the professor.
No partial examinations are given during the semester. A pre-exam in December is possible and usually made available to the students.
The written test, approximately 1.5-to-2 hours long, is typically made of 3-to-4 exercises, mostly based on theory applications and the corresponding analysis and numerical calculations. Some theoretical questions can be included in the written test. Logical order and clarity in answering the questions and solving the problems will be considered in the evaluation. The given classwork is appropriate for testing the expected learning results.
In agreement with the class, up to 2 auto-evaluative class works (about 1 hour each) will be done voluntarily.
A LARGE SELECTION OF PREVIOUS CLASS WORKS WITH SOLUTIONS IS AVAILABLE ON THE COURSE WEBSITE.
The dates of the exams will be notified well in advance by the Professor to the students and published on the web pages of the course.
The official and final exam dates are those published on the School and POLIMI websites.
In the unfortunate event of a student delivering for correction a significantly incomplete classwork, or a severely insufficient one, highlighting a deep lack of knowledge in different and important topics of the course, this may imply the need to skip the next and "near" (at less than one month distance) exam date.
In such an unfortunate case, the professor will very likely use the right to question the student the next time he or she takes and passes a written test.
Prof. Cesare Svelto phone: 02-2399.3610, mobile: 3409761929, e-mail: [email protected]
The written exam, with the assigned exercises & design problems & theoretical questions in it, will be aimed at verifying the student's capabilities also with reference to the following Doublin Descriptors. In particular, the student will/can be asked to provide:
Knowledge and understanding (Dublin Descriptor 1, DD1)
description and analysis of block diagrams of optical measurement setups will be asked;
comparison of different laser sources and their impact on the optical system performance;
analysis of coherent optical detection systems, including signal and noise figures;
explanation about the application of an optical telemeter, interferometer, velocimeter, and optical measurement instrumentation (optical spectrum analyzer, optical power-meter, wavelength-meter).
Applying knowledge and understanding (Dublin Descriptor 2, DD2)
assessment of the operation and performance of available optical measurement instrumentation;
design of an optoelectronic sensor or an optical interferometer;
discussion about pros and cons, as well as performance trade-offs, among different optical measurement instruments.
Making judgements (Dublin Descriptor 3, DD3)
complete analysis of the problem (specific problem and/or project cases in the field of optical measurements), highlighting the peculiar requirements and characteristics, and comparing autonomously different choices in terms of hardware and system performance;
effective written communication, in scientific/engineering language, and reporting individual results and proposals regarding the study and design of optical instrumentation.
Communication (Dublin Descriptor 4, DD4)
reports about the analysis and design of optical instrumentation;
analysis and comparison of different lasers and techniques for optical measurements.
Learning skills (Dublin Descriptor 5, DD5)
design and analysis of optical instrumentation to solve optical measurement tasks;
quantitative analysis of the limiting performance of different and new optical measurements and measurement systems;

Silvano Donati, Photonic Instrumentation: Sensing and Measuring with Lasers, Editore: CRC Press, Anno edizione: 2023, ISBN: 9781032489063
Note: Some 50% of the textbook can be used for a deeper insight of the topics described in the Course. The slides and didactical material, and REGISTRATIONS of lectures and exercise classes, (available on the Course OneDrive website) are more than adequate for a full preparation of the exam.

The first module of the course introduces cameras, visual and infrared spectrum imagers, imaging spectroscopy both theoretically and practically. It contains a review of basic optics to explain the design of a hyperspectral imager. This includes fundamental effects such as refraction, diffraction and interference. With the basic theory established, concepts like spectral range, bandpass, etendue and throughput will be explained. Calibration and correction will be emphasized: Spectral, radiometric, geometric including keystone/smile, and calibration in the field. The second module considers how to design a drone based optical remote sensing system for high quality measurements: Drone based sensor systems, system design, metadata, time synchronization, navigation and geolocation, data compression, onboard and real-time processing systems. Mechanical, electric and software image stabilization, attitude control, gimbals. Methods and software for orthophoto and photogrammetry. The third module gives an introduction to practical drone operations, emphasizing mission planning, risk analysis and safety assurance. The fourth module addresses general challenges in remote sensing with regards to the radiometric effects of the atmosphere and how to compensate for this: Atmospheric effects on remote sensing, Radiative transfer equation, two stream equation, discrete ordinate method, phase functions, application to remote sensing, radiative transfer numerical modelling, spectral properties of targets. The fifth module addresses satellite remote sensing systems. Orbits, attitude control, image acquisition, mission design, mission analysis, correction. The course will give practical training on laboratory calibration, use of cameras and hyperspectral imagers from moving and fixed platforms. The data collected will be evaluated and summarized in a mandatory project report.

The course is designed to provide students of engineering with in-depth skills on Intellectual Property (IP) management, with a special focus on patents.
The students' learning goals are:
Understand challenges, functions, processes in a business and industrial environment and their mutual effects on business, economy, environment and society.
Identify trends, technologies, key methodologies and stakeholder needs in a specific domain.
Design solutions applying a scientific and engineering approach (Analysis, Learning, Reasoning, and Modeling capability deriving from a solid and rigorous multidisciplinary background) coupled with participatory approaches to face problems and opportunities in a business, industrial and societal environment.

Knowledge, applied knowledge and understanding (DdD1 and DdD2):

• Understand the economic and legal rationale of Intellectual Property rights.
• Understand the range of IP tools and the related procedures available to obtain the desired patent coverage based on the set objectives.
• Understand the functioning of international databases to carry out basic patent searches, for monitoring or determining the freedom of operation.
• Identify the content, duration and coverage of a patent document and the freedom to operate that it entails for future or adjacent inventions.
• Understand the sources of uncertainty regarding IPs and the limits and challenges of IP enforcement.
  Autonomy of judgment (DdD3):
• Design suitable strategies of IP coverage and management, taking into account the specific context of the company and its competitive environment.
• Design IP portfolio strategies that match the strategic objectives of the related organization.

The course includes three parts.
PART ONE focuses on the explanation of the economic and legal rationale of IP rights and describes the various protection mechanisms available at the Italian, European and extra-European level, highlighting the main international differences.
Topic 1: What are patents. The economic and the legal-procedural point-of-view.
Topic 2: The requirements of patents (novelty, inventive step, industrial applicability, sufficient description).
Topic 3: The full range of IP rights (copyrights, utility models, trademarks, design protection, and other IPs).
Topic 4: The patenting procedure (filing, examination, extension, granting, opposition, maintenance) of the UIBM, EPO, UPC, PCT and the main international differences.
Topic 5: Practitioner view (guest lecture)
Topic 6: Copyright, trademark
PART TWO focuses on the management of IPs, providing students with a current and far-reaching view on strategies for managing single IPs and portfolios of patents, discussing the strategies currently used by companies in different types of industries, including those that make an open and semi-open use of patents.
The lecutures will also highlight the global trends in infringement and counterfeiting.
Topic 7: Extracting value from IPs. Closed, semi-closed and open IP strategies and how/when to use them.
Topic 8: Patent portfolio management strategies with practical applications.
Topic 9: IP violations: counterfeiting, plagiarism, piracy and infringment. An equitable balance between IP protection and the human rights to a have affordable healthcare.
Topic 10: Methods, circumstances and reliability of IP assessments.
Topic 11: Open innovation
PART THREE focuses on training the students on the use of the databases.
This part includes a series of practical exercises, such as those to check the freedom to operate, the monitoring of competitors, and describes the main steps of prior art searches.
Topic 12: Patent Lab (part I). The patent databases. Search and read basic patent information.
Topic 13: Patent Lab (part II): Freedom to operate, monitoring, preliminary prior art search.
Topic 14: Groupwork: IP Portfolios

Prova scritta obbligatoria, senza prove in itinere
Progetto/i - Elaborato/i obbligatorio, individuale, senza revisioni periodiche, senza presentazione/discussione finale
Student assessment will be based on:
Written homework assignment (10 points)
Written in-class essay exam (20 points)

Robot as a system: component parts; functional model of a robot; robot versus robotics; the operator place (telecontrol, autonomous behaviour). Mechanics: degrees of mobility and freedom; links, joints, wheels and steering; classification of mechanical structures; Cspace. Controlling movements and contacts: geometrical and physical entities; kinematics and dynamics; Cartesian and actuation space; controller using the robot model and trajectory computation; controller using external sensors; control problems in mobile robots. Programming: programming tasks in Cartesian space; simulators and virtual reality for user interface. Assigning tasks to robot: reactive and deliberative architectures; robots as physical agents and the co-ordination problem. World immersion and sensors: kind of sensors and their use as proprioceptive and eteroceptive; sensorial fusion; the role of learning. Images and vision: image acquisition and analysis; from computer vision to robot vision; vision for user interface. Maps, sensors, odometers in mobile robots: path planning; autolocation and natural landmarks.

Il Corso "Personalità Teambuilding e Leadership" introduce lo studente alla conoscenza delle strutture di personalità che orientano l'immagine di Sé, l'atteggiarsi, l'agire, il pensare, l'interagire, il comunicare e il cooperare: per lo studio, il lavoro e le relazioni interpersonali e con particolare riferimento allo sviluppo di competenze per il teambuilding e la leadership. Porta lo studente: - a valorizzare le proprie risorse e migliorare il proprio rendimento nell'acquisizione di nuove abilità e competenze; - a conoscere e ad affrontare meglio situazioni difficili a livello personale o di gruppo; - a prepararsi alla cooperazione e alla leadership; - ad affrontare al meglio le prime esperienze di inserimento lavorativo. Il corso offre laboratori esperienziali per lo sviluppo di soft skill nelle aree trattate, presenta i principi e modelli (di area costruttivista e relazionale) che guidano le esperienze di laboratorio e che possono essere adottati nella futura vita professionale e personale.

The course deals with land and terrestrial surveying techniques by using photogrammetric sensors. At the beginning of the course, the analytical fundamentals of photogrammetry for the sensor orientation and for the object 3D reconstruction will be given. Then, digital images will be introduced together with algorithms for image enhancement and matching. Finally, the main products of photogrammetry, such as orthophotos, will be illustrated. The sensors for data acquisition will be outlined too. Computer laboratory activities will integrate other classes.

This course provides the fundamentals of power electronics. Topics covered include: power semiconductor switches, drive and protection circuits; switching power converters; thermal design. The primary goal of the course is to provide students with the basic skills and knowledge required to analyze and design practical power electronic systems in the low-power range.

Knowledge and understanding (Dublin Descriptor 1)
On completion of this course, students will be able to:
comprehensively understand the principles and operation of modern power semiconductor devices, their limitations, and their driving and protection techniques;
have a sound knowledge and understanding of the operation of switch-mode DC-DC converters used in low power applications;
understand the dynamic performance of dc-dc converters and develop small-signal converter models;
understand and manage the thermal stress of active components in power electronic systems.
Applying knowledge and understanding (Dublin Descriptor 2)
Specific examples and case studies will be provided to make students able to:
select the active/passive components of a power electronic circuit on the basis of specifications;
apply appropriate techniques in the design of low-side and high-side MOSFET drivers;
analyze and design basic protection circuits for power semiconductor devices;
apply appropriate techniques in the analysis of different types of DC-DC converters;
develop and exploit the small-signal model of a DC-DC converter;
design different types of power converters to meet given specifications;
design simple closed loop regulators for DC-DC converters to meet functional objectives;
tackle with steady-state and dynamic thermal performance of power semiconductor devices and ICs.

Power semiconductor switches, drive and protection circuits
Introduction to power electronics. Energy efficiency: the role of power electronics. Basic switching circuits. Overview of power semiconductor switches. Conduction and switching losses.
Schottky and p-i-n power diodes. Reverse blocking. Forward conduction characteristics. Switching characteristics.
Power MOSFETs. Lateral and vertical DMOS structures. ON resistance. Absolute maximum ratings. Dynamic performance. Parasitic capacitances. Gate-charge curve. Switching waveforms. Switching time estimation.
Gate drive circuits for low-side and high-side switches. Bootstrap techniques. Isolated gate drivers. Overvoltage, overcurrent and overtemperature protection circuits.
Switch-mode DC-DC converters
Overview of Distributed Power Architectures (DPA).
DC-DC converters: basic non-isolated topologies (Buck, Boost, Buck-Boost). Pulse Width Modulation (PWM). Steady state converter analysis. Continuous and discontinuous conduction modes (CCM and DCM).
Converter dynamics and control. Circuit averaging and averaged switch modeling. Small-signal modeling of switched-mode power converters. Voltage-mode control of switching power converters: compensator design. Introduction to current-mode control.
Step-down switching regulator design example.
Isolated DC-DC converters. Flyback converter: CCM and DCM operation. Steady state analysis.
Flyback regulator design example.
Thermal management
Thermal considerations for power semiconductors. Heat transfer by conduction, convection and radiation. Packaging of power devices. Heat sinks. Steady state thermal calculations. Transient operation: thermal impedance. Transient thermal calculations.

Prova scritta obbligatoria, senza prove in itinere
Prova orale facoltativa (a scelta dello studente)
Assessment is based on a final exam consisting of a 2-hour, closed-book written test including:

• two numerical problems involving analysis and design (e.g. analysis of switch-mode power converters, power device drive&protection circuits, etc. Selection of active and passive devices for specific power applications) – DD 1,2;
• an open-answer theoretical/practical question on topics taught in the course – DD 1,2.
  The grade of the written exam (highest mark: 30/30) is based on the percentage weightings listed below:
• 45% for each of the two numerical problems;
• 10% for the theoretical question.
  Students who pass the written exam (pass mark: 18/30) may take an optional oral exam, consisting of a discussion on a topic picked at random from this list. The oral exam results in the adjustment (-2/30 to +2/30) of the written exam score, yielding the final grade. “Cum laude” is awarded if the final grade is strictly higher than 31/30.

R.W. Erickson, D. Maksimovic, Fundamentals of Power Electronics, 3rd Edition, Editore: Springer International Publishing, Anno edizione: 2020, ISBN: 978-3-030-43879-1
WeBeep https://webeep.polimi.it/
Note: lecture slides, suggested readings, supplementary course materials and more info available on WeBeep.

The course covers semiconductor devices (Diode, BJT, MOSFET, SCR, IGBT), the main electrical energy power electronic converter types (AC-DC, DC-DC, DC-AC) and the design criteria for sizing converter according to different application.
The goal of the course is to give to students the capability to understand the theory and modeling (average modeling approach) of main power electronic converters, provide the sizing methodology and the basic of control method for power converter.
Students will be able to identify and describe the best solotion for the differen kind of electrical power conversions needed in the modern system using electical energy (industrial, power generation, household application and transportation).
Numerical and practical exercises will complete the theoretical approach.

Lectures will allow students to:

• be able to understand the semiconductor device behavior (linear, switching ideal-not ideal, losses calculation) (DdD1 - capability of knowledge and understanding),
• understand the model of power electronic converter and the working principle (DdD1 - capability of knowledge and understanding),
• use the average modeling method to find the input/output relation (DdD2 - capability of applying knowledge and understanding)
• understand not ideal behavior like not continuous conduction mode, voltage and current ripple (DdD1 - capability of knowledge and understanding)
• be able to choose different power converter topology according to final application. (DdD3 - capability of making judgements, DdD4 - capability of communication);
  The numerical exercises will allow students to:
• understand the sizing methodology
• approach typical design issues of DC/DC and DC/AC converter (component sizing and thermal analysis)
• be able to calculate converter efficiency.
  (DdD2 - capability of applying knowledge and understanding, DdD3 - capability of making judgements);
  The laboratory will allow students to:
• understand microcontroller based control system for power electronic converters
• define basic control rules for power electronic converter and approach the implementation
• test and experience the implemented control rules on small H bridge DC/DC converter.
  Students will be able to identify with topology of power electronics convert is needed in an controller system integration. For automation system for production, renewable energy production and elctrified transportation system.
  (DdD2 - capability of applying knowledge and understanding, DdD3 - capability of making judgements);
  Students will be able also to choose among different power converter architectures for the most efficient, cost effective and safe solution according to the final application in the system.
  (DdD2 - capability of applying knowledge and understanding, DdD3 - capability of making judgements);

Topic.1. Basic of Semiconductor power devices: diode, SCR, MosFet, IGBT, BJT, operating range and losses analysis
Topic.2. AC: Uncontrolled rectifier, controlled SCR based rectifier, single and three phase
Topic.3. Not Isolated DC/DC converter: Buck, Boost, Buck-Boost, SEPIC, discontinuous mode of operating, four quadrant H bridge converter, PFC rectifier.
Topic.4. Isolated DC/DC converter: Flyback, Forward, push-pull.
Topic.5. Inverter: single phase, three phase, PWM and square wave/six step modulation, space vector modulation for three-phase inverter.
Topic.6 Linear power supplies: zener diode based, bjt based, adjustable linear power supplier
Topic.7 Applications of power suppliers
Exercises: Switching and conduction losses, Sizing a not isolated DC/DC converter, Sizing of a DC/AC converter, microcontroller for power electronic
Laboratories: experiment on a 4 quadrant H bridge converter, Buck converter, single phase PWM inverter.

Prova orale obbligatoria
Valutazione continua facoltativa (a scelta dello studente)
The final exam will be in oral form. The exam can be held in July, September or February.
The student during the oral exam will have to be able to:

• describe the working principle of the power electronic converters presented during the course,
• know and describe the different characteristics of semiconductor devices and their working modes (linear, switching, losses),
• find the input/output relation of a converter according to the average modelling approach,
• describe with analytical and critical approach the not ideal behavior of converters such as discontinuous mode and ripple calculation,
• analyze different converter topologies and make a critical description for different application,
• explain the basic of converter sizing (efficiency and losses calculation, component sizing, thermal analysis).
  (DdD2 - capability of applying knowledge and understanding, DdD4 - capability of communication)
  After the exam the student can be able to describe the power converter woking principle for the different applications, help to integrate them in complex system, know hot to implement the control of system includes electrical power conversion via electronic converters.
  (DdD2 - capability of applying knowledge and understanding, DdD3 - capability of making judgements)

N. Mohan, T. M. undeland, W.P. Robbins, Power Electronics: Converters, Applications and Design (Inglese), Editore: John Wiley & Sons Inc; 3 Har/Cdr edizione, Anno edizione: 2002, ISBN: 978-0471226932

This course deals with power systems analyzed from ethical and social perspectives. It is intended to give students a chance to reflect on the ethical, social, and cultural impacts of power systems by focusing on the issues faced by and brought about by electrical engineers.
This integrated course includes lectures by two instructors with complementary expertise, covering the issues of power systems at the intersection of technical and socio-ethical issues, and invited lecturers, covering specific topics at the forefront of research in the socio-technical management of power systems.
Class participation is expected, as classes will be organized as short workshop sessions in which experiences, and individual and group reflections are punctuated by Socratic discussions or microlectures.

Dublin Descriptors
Expected learning outcomes
Knowledge and understanding
Students will:
Acquire a broad perspective on the ethical and social impacts and implications of power systems;
Be acquainted with normative ethics and normative argumentation;
Learn how to recognize and analyze ethical and social aspects and issues inherent in development, control, and design of power systems;
Be able to understand how technical problems are inherently connected to a social dimension within a socio-technical perspective.
Applying knowledge and understanding
Students will:
Be able to use critical skills in clarifying and ethically analyzing case-studies involving power systems;
Be able to apply ethical theories to problems created, aggravated or transformed by smart grids;
Be able to explore and assess possibilities for solving or diminishing existing and emerging ethical and social problems of smart grids.
Making judgements
Students will be able:
To autonomously analyze the ethical issues of a technology and to develop a critical analysis on that;
To evaluate and select the appropriate knowledge in the effort of elaborating and justifying a philosophical argument on a topic autonomously selected.
Communication
Students will learn to:
Work in small groups;
Present in an effective way the results of their independent research, being able to justify their choices.
Lifelong learning skills
Students will:
Be better prepared to their future professional life in an ethically and socially responsible way;
Be able to analyze problems through an ethical lens.

The course will cover different topics from both a theoretical and a more practical point of view.
The following topics will be presented within the context of power systems development and technologies:
Privacy and privacy-preserving analysis of energy data: infrastructure components and consumer devices as data acquiring network devices.
Security both at the individual and national level: power disruption causing loss of infrastructure, endangering public safety and jeopardizing national security
Equity: equitable pricing and affordability of access; fair distribution of costs and benefits; energy management burden on uniformed consumer; managing consumer behavior
Environmental sustainability: future affordability of energy; intergenerational justice; possible conflict between reliability and environmental sustainability.
AI/ML techniques and Big Data analytics for the smart grid: their increasing role and critical issues (e.g., fair algorithmic decision making).
Social acceptance issues: effects of moral values on the acceptance of smart grid technologies; ethics of technology and technology acceptance models.
Professional codes of conduct: benefits and limits; how codes face innovation such as the smart grid.
Students will be supervised in the development of the final project (class presentation) in order to achieve the required standards.

Prova scritta obbligatoria, senza prove in itinere
Progetto/i - Elaborato/i facoltativo (a scelta dello studente), di gruppo, con revisioni periodiche, con presentazione/discussione finale
Foreword: The program is the same for the both versions of the course (i.e, 5 ECTS, and 6 ECTS).
Grading will be on the following basis.
5 ECTS: 50% final project (team presentation in class); 50% written test on the course topics.
6 ECTS: 50% final project (team presentation in class + individual assignment); 50% written test on the course topics. The additional ECTS is accounted for by the individual assignment, which will be announced and agreed upon at the beginning of the course.
Type of assessment
Description
Dublin descriptor
Final project
(class presentation)
Students are required to:
Develop a philosophical argument in which the presented claims are justified by the use of ethical argumentation and the relevant literature;
To present this argument in a presentation in class in front of the instructor and their student colleagues.
1, 3
4, 5
Written test
Questions on the course topics that require to have read and carefully studied the suggested bibliography and the ability to apply it to case-studies;
Questions on the ethical issues associated to power systems requiring the knowledge of the suggested bibliography and the development of critical skills in analyzing technical problems with the tools provided by ethical theories and argumentation.
1, 2
3, 4, 5
Individual assignment (only for 6 ECTS version of the course)
Students are required to:
To present in different forms (e.g., video, class presentation, written text, …) a specific topic related to power systems issues to their peers.
4, 5

The course addresses the problem of control, diagnosis and supervision of production systems. In particular, the Supervisory Control Theory is studied, where the focus is on the automatic synthesis of the controller of plants modelled with labelled automata in presence of any admissible logic specification. In addition, the course will study the automatic synthesis of the diagnoser automaton, able to identify faults based on system observable events. A hint will be given also on the implementation of the supervisor and diagnoser on traditional controllers (PLC). The applications are mainly in the discrete manufacturing field, but also for energy production systems.

The diligent and careful student will learn how to synthesize an automatic controller based on logic specification, within the Supervisory Control Theory, as well as a diagnoser. He will receive a hint on possible implementations. (DdD 1 - Knowledge and understanding; DdD 2 - Applying knowledge and understanding)

Alignment on the basics of automation problems and PLC
Introduction to industrial supervision and control, and formal methododologies
Logic models: labelled automata, formal languages, labelled Petri nets.
Synchronous composition, asycnhronous composition, composition with priorities.
Refinement. Sub-automaton and super-automaton.
Static and dynamic specification. Event-based and state-based specification.
Supervisor properties: controllability, non blockingness.
Synthesis of the supreme controllable supervisor
Implementation of the supervisor. Ladder logic.
Example of a discrete production system supervision.
Monitoring and diagnosis of faults: non deterministic automata.
State observer and event-based diagnosis.
Extensions to hybrid automata: basic modeling, properties, evolution
Hints to applications to energy production systems.

Prova scritta obbligatoria, senza prove in itinere
Prova orale condizionata (a scelta del docente)
Written examination. The exam typically consists of few exercises (3 to 5) with some questions. The duration is approximately less tha 2 hours. The exam will investigate the capabilities of the student to model the behaviour of process and discrete production systems through the formalism of labelled automata (DdD 1 - Knowledge and understanding; DdD 2 - Applying knowledge and understanding), to suitably model the specification on such systems (DdD 1 - Knowledge and understanding; DdD 2 - Applying knowledge and understanding), to synthesize the supremal controllable nonblocking supervisor (DdD 2 - Applying knowledge and understanding), to model a system with basic faults, to identify fault symptoms, to compute the determistic observer for an indeterministic faulty model and finally to synthesize the corresponding diagnoser (DdD 2 - Applying knowledge and understanding). A hint on possible implementations of a basic supervisor can be also asked (DdD 1 - Knowledge and understanding).

Notes on Supervisory Control Theory
Note: Lecture notes, solved exams and exercises available on the course web site

Project topics will be identified during a first meeting with the supervisor. Students are encouraged to contribute their own ideas. Possible topics include sensor and sensor interface design as well as methods and techniques to process (fabricate) them. To realize a project in that area, software like LTSpice, KiCad, Cadence, Scilab or Matlab are used. Furthermore a well equipped laboratory enables experiments and realization of hardware. Students will work individually or in groups.

Necessary computer hardware knowledge. Important issues from operating systems. Analysis of time requirements, resource allocation, principles of resource management within time bounds. Viability analysis and evaluation of requirements for runtime systems for embedded realtime systems. Distributed real-time and data aquisition systems. Analysis and custom for realtime applications. Practical skill in programming of real-time systems.

Elements of Visual Perception, Image Sampling and Quantization, related Mathematical tools applied to Image Processing and Analysis (array, matrix, linear and non-linear operations, arithmetic and geometric operations, morphology, spatial and temporal operations, frequency analysis, linear algebra, probabilistic methods, image transformations and geometric operations) Image Formation: Camera Models, Calibration, Single view geometry, Multiple view geometry, Epipolar geometry, Feature extraction, Bundle adjustment Position and Orientation: Feature based alignment; Pose estimation; Time varying pose and trajectories, Structure from motion, dense Motion Estimation, Visual Odometry (Semi-direct VO, direct sparse odometry) Localization and Mapping: Initialization, Tracking, Mapping, geometric SLAM formulations (indirect vs. direct error formulation, geometry parameterization, sparse vs. dense model, optimization approach), Relocalization and map Optimization, Examples: Indirect (Feature based) methods (MonoSLAM, PTAM, ORB-SLAM), Direct methods (DTAM, LSD-SLAM), Sensor combinations (IMU, mono vs. Stereo, RGB-Depth), Analysis and parameter studies Recognition and Interpretation: Object detection, Instance recognition, Category recognition, Context and Scene understanding

The course aims to expose students to the challenges involved in developing realistic robotic applications within an industrial systems perspective. It will provide an in-depth understanding of how robotic systems are integrated with other automation components and industrial vision systems, highlighting both the technical and practical aspects required to design, implement, and manage robotized solutions. Emphasis is placed on the interaction between robots, sensors, actuators, and vision systems, as well as on the synchronization and coordination challenges that arise in real-world automation scenarios.

At the end of the course, the students will be able to:
Understand requirements and tools to design and deploy a robotized industrial process (DD1, DD3)
Program an industrial robot for realistic industrial applications (DD2, DD5)
Integrate robots and sensors (e.g., cameras, limit switches) to deploy a robotic application (DD2)
Take decisions on design aspects (hardware and software) to effectively implement open-world robot solutions (DD3)
Communicate the design and implementation choices to their peers under the form of written reports and technical presentations (DD3)

Main topics of the course:
Robot programming (from Teach Pendant and remote interfaces)
Design and calibration of EOAT (End of Arm Tooling)
Calibration of objects reference systems
Interfacing sensors (e.g. photoelectric sensors, limit switches) and actuators (e.g. electric grippers)
Synchronization issues between the robot and the process
Use and calibration of vision systems to locate objects and manipulate them
Implementation of safety features (virtual walls, protective stops)

Progetto/i - Elaborato/i obbligatorio, di gruppo, con revisioni periodiche, con presentazione/discussione finale
Valutazione continua condizionata (a scelta del docente)
At the beginning of the course, the students will be grouped in teams.
The final exam consists of a technical report and a final presentation to the whole classroom during which the groups will demonstrate that:

• They properly understood the requirements of the project and translated them into technical specifications (DD1,DD3).
• They implemented adequate methods and tools to address the problems at hand (DD2, DD3).
• They experimentally validated the methods on the available hardware (DD2).
• They are able to critically analyse numerical and experimental results (DD3,DD5).
• They are able to summarize and present their work in written and oral form (DD4).
  The final evaluation will also take into account the student approach to the project work and to the team activities during the laboratory hours.

B. Siciliano, L. Sciavicco, L. Villani, G. Oriolo, Robotics: Modelling, Planning and Control, Editore: Springer, ISBN: 9781846286414 http://www.springer.com/engineering/robotics/book/978-1-84628-641-4
Kevin M. Lynch and Frank C. Park, Modern Robotics: Mechanics, Planning, and Control, Editore: Cambridge University Press, ISBN: 9781107156302 http://hades.mech.northwestern.edu/index.php/Modern_Robotics

The final goal of the course is to provide methods to control uncertain linear dynamical systems.
Three main steps are needed to lead the students to such a goal.
First, techniques are provided for the shaping and quantification of the uncertainties, in terms of suitable definition of system norms.
Second, the problem of robust stability at the face of uncertainties are tackled via a Lyapunov function approach.
Finally the problem of feedback control of uncertain systems is deal with through parametrization and model matching synthesis.
Some classical, as well as modern techniques, will be covered so that the student will acquire the methodological skills to tackle a wide corpus of robust analysis and robust control problems for uncertain systems.
The theoretical and exercise lessons are complemented with laboratory sessions using Matlab/Simulink to test some topics of the course.
DD1 – Knowledge and Understanding
Students will acquire advanced knowledge of modelling uncertainties in linear dynamical systems, robustness measures based on system norms, Lyapunov-based methods for robust stability analysis, and methodologies for robust controller synthesis. They will understand both classical and modern approaches to robust analysis and control design, together with their theoretical foundations and applicability conditions.
DD2 – Applying Knowledge and Understanding
Students will be able to model uncertain dynamical systems, quantify uncertainty using appropriate mathematical tools, assess robust stability properties, and design feedback controllers that guarantee prescribed performance in the presence of uncertainty. They will be able to apply robust control techniques to engineering problems and validate the obtained solutions through numerical simulations and software tools such as Matlab/Simulink.
DD3 – Making Judgements
Students will be able to critically evaluate modelling assumptions, uncertainty descriptions, and alternative robust-control design methodologies. They will develop the ability to select appropriate analysis and synthesis techniques based on system characteristics, performance requirements, computational complexity, and robustness specifications.

Lectures, exercise, and lab sessions will allow students to:
Compute the norms of a dynamical system
Check robust stability
Define a robust control problem in terms of modelization of uncertainties
Solve a robust control problem via parametrization and model matching
Learn specific Matlab toolboxes (control & optimization) to work out suitable algorithms based on Linear Matrix Inequalities for the points above.

The course is structured in two parts, the first one dealing with analysis and the second one with design.
Part 1: Robust performance analysis of uncertain systems

• Norm bounded uncertainties and system norms (H2-Hinf)
• Polytopic uncertainties and switching
• Checking robust stability via linear matrix inequalities (LMIs)
• Passivity
• Application examples
  Part 2: Robust performance design of uncertain systems
• Robust H2 control and filtering
• Robust Hinf control and filtering
• Switching control (sketch)
• Robustness in digital implementation (sketch)
• Application examples

Prova scritta condizionata (a scelta del docente), senza prove in itinere
Prova orale condizionata (a scelta del docente)
Progetto/i - Elaborato/i condizionato (a scelta del docente), di gruppo, senza revisioni periodiche, senza presentazione/discussione finale
Valutazione continua condizionata (a scelta del docente)
The course comprises lectures and specific sessions devoted to illustrative examples and the use of computational tools.
The final assessment consists of a written or oral examination covering the theoretical contents of the course.
The assessment is aimed at verifying:
DD1 (Knowledge and Understanding): understanding of uncertainty modelling techniques, robust stability analysis methods, and robust controller synthesis approaches for linear dynamical systems;
DD2 (Applying Knowledge and Understanding): ability to formulate and solve analysis and control-design problems for linear systems affected by polytopic and norm-bounded uncertainties using the methodologies introduced in the course;
DD3 (Making Judgements): ability to evaluate modelling assumptions, select appropriate robust-control methodologies, and assess the robustness properties of the proposed solutions.
Students are expected to demonstrate the ability to use the acquired theoretical tools to analyse and design control systems for uncertain linear dynamical systems.

Patrizio Colaneri, Jose' C. Geromel, Arturo Locatelli, Control Theory and Design: an Rh2 and RHinf viewpoint, Editore: Academic Press, Anno edizione: 1997
Note: Only for a few chapters
Okko H. Bosgra, Huibert Kwakernaak, Gjerrit Meinsma, Design Methods for Control Systems
Paolo Bolzern, Patrizio Colaneri, Arturo Locatelli, Controllo e filtraggio in H2-Hinf, Editore: Polipress
Patrizio Colaneri, Notes on Robust Control
Note: In preparation

The first part of the course is aimed at allowing the student to learn and apply the main techniques for the hazard and reliability analysis of industrial and engineering systems, such as Preliminary Hazard Analysis, Failure Mode and Effect Analysis, Fault Tree Analysis. In the second part, the course focuses on the most widely used methods for the fault detection and diagnosis of complex systems. In particular, some data-driven methods are first discussed, such as control charts, principal component analysis, support vector machines, neural networks. Analytical fault detection techniques based on state observers, parity space equations, recursive estimation algorithms are then presented. Finally, some methods for the design of fault tolerant control systems are described. All these methods are applied to some realistic test cases to witness their potentialities and limitations.

Knowledge and understanding (DdD1)
Upon positive completion of the course, the students will be able to:
define the main steps of the System Hazard Analysis (SHA) of an engineering system;
perform the functional and architectural analysis of the system, and define the elements of SHA, such as its main reliability indices, operating modes, targets, risks;
apply the main SHA methods used in industry, such as Preliminary Hazard Analysis, Fault Tree Analysis, Failure Modes and Effects Analysis;
perform the fault detection analysis of dynamic systems subject to sensors, actuators, and parametric faults, based on data-driven (statistical models, support vector machines, neural networks) and analytical methods (parity space equations, unknown input observers, Binary Phase Detection Filters);
design fault tolerant controllers for systems subject to faults.
Applying knowledge and understanding (DdD2)
Through the exercise and lab sessions, students will learn to:
complete the SHA of a complex engineering system;
select the proper method for fault detection based on the available knowledge of the considered system and its characteristics;
given a system in fault, determine the achievable performance and the most appropriate algorithm for fault detection and fault tolerant control.
Making judgements (DdD3)
Upon positive completion of the course, students should be able to
perform a detailed SHA, guaranteeing given Safety Integrity Levels (SIL);
design a fault detection procedure based on data-driven and analytic methods, or a mix of them;
evaluate the limits to the achievable performance of the system in fault;
critically evaluate the achieved results and their sensitivity to the adopted design choices.
Communication skills (DdD4)
Through software labs and the development of a specific project, which is part of the exam, students will learn how to collaborate and communicate in a clear, concise, and effective way the addressed problem, the chosen solution method and the obtained results.
Learning skills (DdD5)
Through software labs and the development of the project, students will learn to address, autonomously and within a working group, a real-world problem via the advanced SHA and Fault Detection methods learned in the course.

Introduction to the safety analysis of industrial systems.
Preliminary Hazard Analysis and HAZard and OPerability analysisin automation systems.
Risk analysis techniques: Failure Mode and Effect Analysis, Fault Tree Analysis, Event Tree Analysis, Cause Consequence Analysis.
Introduction to the fault detection problem: data-driven and analytical redundancy methods, qualitative approaches.
Statistical Quality Control, control charts, principal component analysis.
Data-driven algorithms for fault detection, such as those based on support vector machines and neural networks.
On-line diagnosis with parity space equations, state observers, parameter estimation.
Design of fault tolerant control systems.

Prova scritta obbligatoria, senza prove in itinere
Progetto/i - Elaborato/i obbligatorio, di gruppo, con revisioni periodiche, con presentazione/discussione finale
The exam is divided into two parts. In the first one the student is required to complete the hazard analysis of an engineering system. The project must be submitted in power point or pdf form and can be developed by groups of 2-3 students (DdD1, DdD2, DdD3, DdD4).
The second part, referred to fault detection techniques, consists of a written exam.
In the written exam students must prove to be able to apply to simple numerical examples the main fault detection methods, and in particular:
Select the proper fault detection technique based on the system’s characteristics (DdD2).
Apply the selected design method and critically evaluate the achieved results and their sensitivity to the adopted design choices, such as thresholds selection (DdD3).
Describe the achieved results in a clear and convincing way (DdD4, DdD5).

Slides
Note: http://corsi.metid.polimi.it/
A. Villemeur, Reliability, availability, maintainability and safety assessment, Editore: Wiley & Sons, Anno edizione: 1991
L.H. Chiang, E.L. Russel, R.D. Braatz, Fault detection and diagnosis in industrial systems, Editore: Springer, Anno edizione: 2001
J.J. Gertler, Fault Detection and diagnosis in engineering systems, Editore: Marcel Dekker, Anno edizione: 1998

The course is aimed to deepen the concepts of dynamic modelling and simulation, and to explain the architecture of the most popular relevant software tools. To be more specific, the course is aimed to provide students with the skills needed to address problems of modeling and simulation of complex engineering systems. The first part will be dedicated to illustrate the causal approach to modeling. The second part of the course will be devoted to introducing the acausal approach, based on object-oriented programming concepts, languages and tools. In particular, in this second part, the characteristics of the modeling language Modelica will be illustrated, now standard "de facto" for the modular, multi domain modelling. The characteristics of the object-oriented modelling and simulation environments will be illustrated in reference to some application cases in a computer laboratory. These application cases will include examples of multidomain engineering and control systems.

Lectures will allow students to:
Understand the theory of numerical methods for the solution of systems of ordinary differential equations (ODE) (DdD 1).
Model and numerically solve hybrid dynamic models (events) (DdD 2).
Understand the basic theory of differential-algebraic equations systems (DAE) (DdD 1).
Use an object-oriented modelling language (Modelica) to model complex engineering systems (DdD 2).
Understand symbolic manipulation techniques applied to DAE system in order to derive efficient simulation codes (DdD 1).
Exercises will allow students to:
Build simulation models of complex systems in acausal (OpenModelica) object-oriented modelling environments (DdD 2).
Describe and numerically solve hybrid dynamical systems (DdD 2).
Select solver parameters and interpret the results of the simulations in terms of accuracy and computational efficiency (DdD 2).
After passing the exam, requiring the development of a simulation model, students will be able to:
Make design choices when building a simulation model (DdD 3) (DdD 5).
Justify design choices when building a simulation model (DdD 4).
Communicate the results of the work (DdD 4).

Lectures
1 Introduction.
1.1 System, experiment, model, simulation.
2 Causal approach.
2.1 Causal models and ODE.
2.2 From models to simulations.
2.3 IVP (Initial Value Problem)
2.3.1 Theorem of global existence and uniqueness of an IVP: hypothesis.
2.3.2 Theorem of global existence and uniqueness of an IVP: thesis.
2.3.3 Stability of an IVP.
2.3.4 Stability of the problem and numerical integration.
2.3.5 Numerical integration and stability of the solution of an IVP.
2.4 Elementary methods.
2.4.1 Forward Euler method.
2.4.2 Local truncation error and difference operator.
2.4.3 Consistency and convergence.
2.4.4 0-stability.
2.4.5 Fundamental convergence theorem.
2.4.6 0-stability of the forward Euler method.
2.4.7 Absolute stability.
2.4.8 Region of absolute stability.
2.4.9 Stiffness.
2.4.10 Backward Euler method.
2.4.11 Newton's method.
2.4.12 Modified Newton's iteration.
2.4.13 Trapezoidal method.
2.4.14 Midpoint methods.
2.5 Managing discontinuities.
2.6 Local error and tolerances.
2.7 Methods of step size selection.
2.7.1 Local error estimation: step doubling.
2.7.2 Local error estimation: embedded methods.
2.8 Higher order methods.
2.8.1 General formulation of Runge Kutta methods.
2.8.2 Accuracy, 0-stability and absolute stability regions of Runge-Kutta methods.
2.8.3 Embedded methods.
2.8.4 Implicit Runge-Kutta methods.
2.8.5 Linear multistep methods.
2.8.6 Adams-Bashforth methods.
2.8.7 Adams-Moulton methods.
2.8.8 BDF methods.
2.8.9 0-stability of multistep methods.
2.8.10 Absolute stability of multistep methods.
2.8.11 Predictor-corrector methods.
2.8.12 Milne's estimate.
3 Acausal approach.
4 Introduction to the Modelica language.
4.1 Classes, equations, parameters, constants.
4.2 Records, annotations, types.
4.3 Connectors.
4.4 Inheritance and partial models.
4.5 Events.
4.5.1 Synchronous data-flow principle.
4.5.2 Conditional models.
4.5.3 noEvent().
4.5.4 Discrete event models.
4.5.5 Events synchronization.
4.6 Stream variables and connectors.
4.7 Packages.
4.8 Components and (sub)systems.
4.8.1 Architecture driven approach.
4.9 Expandable connector.
5 DAE systems.
5.1 Examples.
5.2 Differentiation index.
5.3 Initial conditions and constraints.
5.4 Linear constant coefficient DAE systems.
5.5 Kronecker canonical form.
5.6 Algebraic index.
5.7 Index and numerical integration.
5.8 Dummy derivatives method.
5.9 Numerical methods for DAE systems.
5.9.1 Direct discretization methods: backward Euler, BDF, Radau.
6 Modelica code translation.
6.1 Hybrid DAE system.
6.2 Structural analysis.
6.2.1 Bipartite graphs.
6.2.2 Duff Algorithm.
6.2.3 Pantelides theorem.
6.2.4 BLT reordering.
6.2.4 The tearing problem.
Exercises
All exercises will be carried out in a computer laboratory
1. Model of a brushless motor in Modelica.
2. Discrete event models: event iteration.
3. Stream connectors. Control of a mixer.
4. Digital control systems in Modelica. Conditional connectors.
5. Discontinuous friction model. Stick-slip and hunting motions.
6. Conditional components: modelling and simulation of a mechanical stop.
7. Multiphysics modelling: model of an hydraulic piston.
9. Multibody package.

Prova orale obbligatoria
The evaluation test will be both practical and oral and it will be divided into two parts:
In a first part the student will be asked to develop "from scratch" and simulate the model of a freely chosen dynamic system, in an acausal object-oriented modelling environment (either OpenModelica or Dymola). The complexity of the model, the mastery of the modeling tool and the ability to interpret the results will constitute elements of judgment (DdD 1 - DdD 2 - DdD 3 - DdD 4 - DdD 5).
In the second part the student will be asked to show his/her knowledge of the theoretical contents of the course, answering theoretical questions or solving simple exercises. Examples of possible questions can be found in the texts of written exams assigned in the past, available on the course's WeBeeP page (DdD 1 - DdD 2).

U. M. Ascher, L. R. Petzold, Computer Methods for Ordinary Differential Equations and Differential-Algebraic Equations, Editore: SIAM, Anno edizione: 1998, ISBN: 978-0-89871-412-8
Modelica by Examples https://mbe.modelica.university/
Modelica Tutorial https://openmodelica.org/images/M_images/250204-ModelicaTutorial-PeterFritzson-AdrianPop-MODPROD-2025.pdf
F. E. Cellier, E. Kofman, Continuous System Simulation, Editore: Springer US, Anno edizione: 2006, ISBN: 978-1-4419-3863-3

The course integrates the content provided by the courses Smart Maintenance Management (SMM, 5 credits ) and Industrial Asset Management (IAM, 5 credits ); as a whole, it is delivered as Smart Maintenance and Industrial Asset Management (SM&IAM, 10 credits ).
The goal of the SMM course ( first half of the semester ) is to introduce the student to a modern maintenance practice and advanced solutions for increased productivity, today vital for companies in every sector. The maintenance is indeed experiencing a radical evolution, moving towards an advanced management of the physical assets in order to guarantee the reliability, availability, efficiency, quality, safety, costs and sustainability in the operations of the physical assets and systems. The course will particularly address this evolution in the context of industrial plants in discrete manufacturing and process industry.
The goal of the IAM course ( second half of the semester ) is to introduce the student to Industrial Asset Management, intended as an integrated methodology to optimize the management of the physical assets along their lifecycle. Currently defined as “the coordinated activities of an organization to realize value from assets” (ISO 55000), Asset Management provides a major focus on the long-term strategic perspective, orchestrating decisions and activities in various asset lifecycle stages (including design, commissioning, maintenance, operation, decommissioning). This is becoming popular in the industrial world, where attention is given to the physical assets, equivalently referred to as industrial assets. The course will then address the evolution of the discipline in the management across different sectors, including manufacturing plants, fleets of assets, and infrastructures as exemplary cases of application.
The SMM course fits into the overall program curriculum, pursuing some of the defined general learning goals and, in particular, contributing to the development of the following capabilities:
Understand context, functions, processes, resources and decisions of industrial maintenance management, and their impacts in terms of asset-related performances, costs and risks;
Identify trends, technologies and methodologies to support decision-making to manage maintenance based on data and evidences from field, while orienting to the strategic requirements due to operations of the industrial plants and the business needs;
Design solutions for industrial maintenance management built on the smart application of an engineering approach to face problems and opportunities in a business and industrial environment.
The IAM course fits into the overall program curriculum, pursuing some of the defined general learning goals and, in particular, contributing to the development of the following capabilities:
Understand context, functions, processes, resources and decisions of industrial asset management, and their impacts in terms of asset-related performances, costs and risks;
Identify trends, technologies and methodologies to support decision-making to manage the lifecycle of industrial assets, in light of the stakeholders’ requirements and objectives to balance performances, costs and risks;
Design solutions for industrial asset management, to orient towards realizing a sustainable value from assets based on smart approaches to balance performances, costs and risks in a business and industrial environment.
The SM&IAM course as a whole aims at providing knowledge and understanding, both considering theory and practical abilities, for what concern the asset-related decision making in the maintenance and the industrial asset management practice.

Students will learn contents and practices according to what defined in the leaning objectives. The learning is correspondingly organized considering the course delivery in SMM and IAM thematical areas.
Smart Maintenance Management (SMM)
The expected learning outcomes of the SMM course - first half of SM&IAM course, achievable through lectures and exercise/case study sessions, consist of knowledge of the criteria and methods to support decisions for industrial maintenance management, and of the practical abilities to analyze performances, costs and risks to support a smart maintenance decision-making. In particular, the course will allow students to achieve knowledge and practical abilities in order to:
Understand the role of industrial maintenance management for what concerns its impacts in terms of reliability, availability, efficiency, quality, costs, and sustainability;
Distinguish different contexts and requirements identified in specific problem settings, eventually being able to understand problems and opportunities in a given business and industrial environment and to formulate the goals in terms of performances, costs and risks addressed by the physical assets;
Identify trends, technologies and methodologies to support decision-making to manage maintenance based on data and evidences from field, while orienting to the strategic requirements due to operations of the industrial plants and the business needs;
Analyze the performances, costs and risks impacted by a smart management approach in industrial maintenance management, through the use of key enabling technologies with particular emphasis on data analytics;
Design solutions for a smart management approach in industrial maintenance, considering contexts and requirements due to specific problem settings, while targeting performances and costs, and the requirements for risk mitigation.
Building on the theory and through problem-solving in exercise/case study sessions, students will acquire the skills to formulate a judgment, meaning to distinguish specific problem settings, analyze the data sets provided with these settings, and subsequently design solutions for cost-effective decisions in maintenance management.
Industrial Asset Management (IAM)
The expected learning outcomes of the IAM course - second half of SM&IAM course, achievable through lectures and exercise/case study sessions, consist of knowledge of the criteria and methods to support decisions for industrial asset management, and of the practical abilities to analyze performances, costs and risks to support a smart decision-making in the asset lifecycle. In particular, the course will allow students to achieve knowledge and practical abilities in order to:
Understand the role of industrial asset management for what concerns its impacts in terms of reliability, availability, efficiency, quality, costs, and sustainability;
Distinguish different contexts and requirements identified in specific problem settings, eventually being able to understand problems and opportunities in a given business and industrial environment and to formulate the goals in terms of performances, costs and risks addressed by the physical assets;
Identify trends, technologies and methodologies to support decision-making to manage the lifecycle of industrial assets, in light of the stakeholders’ requirements and objectives to balance performances, costs and risks;
Analyze the performances, costs and risks impacted by a smart management approach in industrial asset management, through the use of key enabling technologies with particular emphasis on data analytics and simulation (including novel perspectives taken from virtualization of industrial assets also known as Digital Twins);
Design solutions for industrial asset management, considering contexts and requirements due to specific problem settings, with the purpose to orient towards realizing a sustainable value from assets based on smart approaches to balance performances, costs and risks in a business and industrial environment.
Building on the theory and through problem-solving in exercise/case study sessions, students will acquire the skills to formulate a judgment, meaning to distinguish specific problem settings, analyze the data sets provided with these settings, and subsequently design solutions for cost-effective decisions in asset lifecycle management.

The course contents are delivered through lectures and exercise/case study sessions, and invited speakers from different industrial settings. The course delivery is correspondingly organized in SMM and IAM thematical areas.
Smart Maintenance Management (SMM)
This thematical area focuses on maintenance and its evolution towards an advanced management of the physical assets in industrial contexts. The evolution builds on reliability and maintenance engineering approaches adopted in different industrial sectors (Total Productive Maintenance and Reliability Centred Maintenance), presented though methods and techniques oriented to the risks mitigation, performance improvement, and costs optimization and saving. Innovative aspects are also addressed, including the exploitation of diagnostic and prognostic technologies in advanced architectures due to the digitization and automation, and the adoption of new management concepts leading to a data-driven decision-making and an augmented intelligence to support the operators. Impacts of maintenance will be evaluated in terms of reliability, availability, efficiency, quality, and costs in the operations of the assets; impacts for sustainability will be also considered through sustainability-related Key Performance Indicators and maintenance practice for their improvement.
The following topics are correspondingly presented and discussed during the course (SMM course - first half of SM&IAM course):
Maintenance concepts evolution towards advanced management
Basics of Reliability, Maintainability, Availability (RAM) analysis
Maintenance policies and strategies
Maintenance costs, maintenance policies and strategies, and costs optimization
Maintenance impacts and contributions for sustainability
Reliability and Maintenance Engineering approaches, methods, techniques and models
Prognostics and Health Management for Condition Based and Predictive Maintenance programs
Advanced maintenance management practice built on the Key enabling technologies due to digitalization and automation
Industrial Asset Management (IAM)
This thematical area focuses on the evolution of Asset Management, looking at the capabilities integrated within an industrial organization when Asset Management is adopted. These capabilities serve to achieve a systematic and coordinated practice in order to sustainably manage the organization’s physical assets, and their associated performances, risks and expenditures over the lifecycles, with the purpose of contributing to the achievement of the organizational strategic plan. To enable this practice, different methodologies, methods, tools and Key Performance Indicators (KPIs) are required to develop an integrated approach including Asset Performance Management, Total Cost of Ownership and Risk assessment of the assets in the built and industrial environment. Moreover, the management of resources for logistic support of the physical assets and systems (including materials management and maintenance services) are considered as key lever for the sustainable management. Eventually, the use of the key enabling technologies due to the digitalization is discussed, presenting the information management along the asset life cycle, and including management and engineering tools specifically adopted for Asset Management.
The following topics are correspondingly presented and discussed during the course (IAM course - second half of SM&IAM course):
Industrial asset management and its evolution towards advanced management
Asset Management system and its integration in the industrial organization
Reliability and Maintenance Engineering approaches, methods, techniques and models
Asset performance management (APM): i) methodologies, methods and tools for APM from individual asset-level to system-level assessment; ii) key performance indicators for APM from individual asset-level to system-level assessment (Overall Equipment, Line and Factory Effectiveness)
Cost and risk assessment of physical assets (including Total cost of ownership, Performance-driven Total cost of ownership, Asset criticality assessment)
Management of the resources required for logistic support of physical assets: MRO (maintenance, repair and operations) materials management and maintenance services
Industrial asset management practice in different industries
Information management along the asset life cycle, Key enabling technologies and tools

Prova scritta obbligatoria, senza prove in itinere
Prova orale obbligatoria
The examination consists of a written examination on the content of the lectures and exercise/case study sessions, and of an oral examination. The written examination will be held in two parts aimed at assessing the achievement of the learning process in SMM and IAM topics. During the examination, the student should demonstrate knowledge of theory and practical abilities in setting and solving problems in the scope of SMM and IAM topics. To pass the course, students must successfully pass the written and oral exam.
More specifically, the students will have to demonstrate, during the exam, their learning outcomes as knowledge of the criteria and methods to support smart decisions for industrial maintenance management (SMM course - first half of SM&IAM course) / industrial asset management (IAM course - second half of SM&IAM course), as well as the practical abilities to analyze performances, costs and risks to support the decision-making (all courses). In particular, during the exam, the students will have to:
be able to name and define, with precise terminology, context, functions, processes, resources and decisions within the scope of industrial maintenance management / industrial asset management;
be able to recognize and discuss the role of industrial maintenance management / industrial asset management for what concerns its impacts in terms of reliability, availability, quality, safety, costs, and sustainability;
be able to describe and explain the main performances, costs and risks impacted by a smart approach in industrial maintenance management / industrial asset management;
be able to describe and explain the main criteria, modeling techniques / methods, and methodologies to support a smart approach in industrial maintenance management / industrial asset management;
be able to identify, describe and analyze the use of key enabling technologies for a smart decision-making support based on data analytics and simulation techniques;
be able to distinguish different contexts and requirements identified in specific problem settings, eventually being able to understand problems and opportunities in a given business and industrial environment and to formulate the goals in terms of performances, costs and risks addressed by the industrial assets;
be able to analyze the data sets provided within specific problem settings, to assess and/or to predict the related performances, costs as well as the risks due to uncertainties in attainment of the goals;
be able to design solutions for a smart approach in industrial maintenance management / industrial asset management, considering contexts and requirements due to specific problem settings, while targeting performances and costs, and the requirements for risk mitigation.

A. Crespo Marquez, The Maintenance Management Framework. Models and Methods for Complex Systems Maintenance, Editore: Springer, Anno edizione: 2007
Andrew K.S. Jardine, Albert H.C. Tsang, Maintenance, Replacement, and Reliability. Theory and Applications, Editore: CRC Press, Anno edizione: 2013
J. Lee, Industrial AI. Applications with Sustainable Performance., Editore: Springer, Anno edizione: 2020
L. Furlanetto, M. Garetti, M. Macchi, Principi Generali di Gestione della Manutenzione, Editore: FrancoAngeli, Milano, Anno edizione: 2006
L. Furlanetto, M. Garetti, M. Macchi, Ingegneria della manutenzione, Editore: FrancoAngeli, Milano, Anno edizione: 2007
L. Furlanetto, M. Garetti, M. Macchi, Pianificazione, organizzazione e gestione tecnico-economica della manutenzione, Editore: FrancoAngeli, Milano, Anno edizione: 2011
A. Crespo Márquez, M. Macchi, A. K. Parlikad, Value Based and Intelligent Asset Management: Mastering the Asset Management Transformation in Industrial Plants and Infrastructures, Editore: Springer, Anno edizione: 2019
J. E. Amadi-Echendu, K. Brown, R. Willett, and J. Mathew, eds., Definitions, concepts and scope of engineering asset management, Editore: Springer, Anno edizione: 2010
J.D. Campbell, A.K.S. Jardine, J. McGlynn, Asset management excellence: optimizing equipment life-cycle decisions, Editore: CRC Press, Anno edizione: 2016
N.A.J. Hastings, Physical Asset Management, Editore: Springer, Anno edizione: 2010
Lecture slides and tutorials https://beep.metid.polimi.it

Methodical and application oriented knowledge to determine the most important physical parameters (temperature, light/color, motion) will be advanced and linked to the field of CMOS-integration. Equipped with this knowledge, students are able to solve state of the art problems from industry and science based on typical parameters and calculations.
In the module (IV) "Smart Sensors and Actuators" silicon- and CMOS-based sensors and actuators are considered. This includes basics of solid state physics, operating principles, embodiment and characteristics, as well as suitable design approaches for integrated circuits for readout and operation of such sensors and actuators. Covered topics are: integrated temperature sensors, PTAT- (proportional to absolute temperature-) circuits, accurency-limiting artifacts and their compensation, electrothermal filters, photodiodes and photodiode arrays for CMOS cameras, CCDs, active pixels and their operating principles, CMOS imaging, accelerometers and gyroscopes, MEMS technology, digital mirror arrays, and DLP technology for projectors. Lecture language is English.

The course “Smart Wearables Design and Prototyping” provides advanced electronics design techniques for prototyping a smart wearable system by means of hands-on learning. Throughout the course students will become familiar with devices, processors, sensors and communication protocols used in low-power and small size wearables. The course addresses the fundamental concepts and tools for developing sensing electronics and low-power embedded processing, within the new cross-disciplinary Major on “Smart Wearable Technologies”. Case studies of industrial interest will be discussed.
The course includes 12 hours of theoretical basics for the design of smart wearables, including key design guidelines and architecture as well as examples and use cases, and 38 hours of design and hands-on laboratories, during which teams of 5 students will prototype their own smart eyewear, learning the whole process flow, from the PCB design, to the microcontroller firmware and smartphone app development.
Starting from templates of main-board (with STM32U5 microcontroller, BLE, flash, IMU, USB-C), firmware, glasses frame, smartphone app, each team will prototype a smart-eyewear, by designing sensors-probe (e.g. PPG, IMU, barometer, photodetectors, microphone), firmware, and smartphone app to display acquired data.

Knowledge and understanding (Dublin Descriptor #1)
Students will learn:
the architecture of a portable low-power electronic system
the working principle of key solid-state sensors and their interfaces
to design and properly size a sensor acquisition chain
the architecture and programming of an ARM microcontroller
the potential and constraints of embedded machine learning
to integrate the electronics with mechanics and software
Applying knowledge and understanding (Dublin Descriptor #2)
Students will apply their knowledge in practical case studies:
design of a PCB with sensors to be integrated in the frame
develop the firmware and software needed to acquire and process the sensors data
perform measurements and analyze the data
Making judgements (Dublin Descriptor #3)
Given a practical case study and thanks to hands-on experience, students will:
analyze the critical issues
select the best configurations and components to be used
select the best processing solution
think the best way to validate the system
Communication (Dublin Descriptor #4)
Students will learn to:
work in team
express and justify their project choices
Lifelong learning skills (Dublin Descriptor #5)
Students will be enabled to
read and understand sensors and electronic components data-sheets
develop an electronic project, starting from PCB design to realization and characterization

Smart Wearables Architectures
Introduction: course organization, design and hands-on lab activities team work
Smart wearable architecture, design, challenges (size, power consumption, embedded machine learning) and case-studies (eye tracking, biosensing, context awareness...)
Electronics development
Guidelines for PCB design (materials, power and signal routing, decoupling, high frequency path and termination)
Altium Designer layout CAD (schematic and layout, PCB stack, libraries, rigid/flex, gerber output standards)
Wearable Sensors (image sensors, MEMS…)
Batteries and power management fundamentals
Detailed schematics and layouts of main-board and sensing-probes to be used in the Labs
Design of the sensing probes (board layout, revision, generation of files for manufacturing)
Firmware development
Wearable and Low-Power Computing (MCU, embedded processing)
Communications (wired and wireless protocols)
Cube environment for programming STM32U5 (DMA, interrupts, HAL_functions, peripherals) with ultra low-power modalities
Detailed code of main-board and firmware modules (memory, USB and BLE communication, I2C communication with IMU, hands-on trials)
Design of the firmware for the sensors-probe (interfacing, data analysis, tests)
App development and project validation
Flutter tools for developing Apps (from a provided template)
Design of custom smartphone App (final tests)
Customization of the App to display data from the sensing-probe

Prova scritta obbligatoria, senza prove in itinere
Prova orale obbligatoria
Progetto/i - Elaborato/i obbligatorio, di gruppo, senza revisioni periodiche, senza presentazione/discussione finale
Assessment will be based on:
A report on the project developed in teams (individual contributions will be taken into account).
A written exam covering the entire course content (both the theoretical topics and the developed project). The written exam will include both multiple-choice and open-ended questions.
A short oral examination based on the written exam and the project report.
Technical knowledge, critical analysis of the design choices, and communication skills will all be evaluated.
The project report and the written exam will each account for 50% of the final grade. The oral examination will be used to verify each student's individual contribution to the project and to clarify any ambiguous or uncertain aspects of the written exam.

Franco Zappa, Microcontrollers, Editore: Società Editrice Esculapio, ISBN: 9788893850223

The objective of the course is to address the problems, solutions, methods, and technologies for big data storage and management, with special emphasis on scalability and persistency. The course is structured in three main parts. The first part covers the main principles and approaches of big data management, spanning issues like scalability, transactionality, and distribution of data. The second part covers the different approaches to unstructured data management, describing data models, query languages, and architectural solutions for non-relational data storage, also known as NoSQL solutions, spanning graph, columnar, documental, key-value, and IR-based databases. The third part discusses the design methodologies for the specification of NoSQL data models.

Students will learn the basic concepts of modern database approaches. They will become able to design, program, and use the different database methods, and they will learn how to select and adopt the best option depending on the business and technical requirements.
Dublin Descriptors
Expected learning outcomes
Knowledge and understanding
Students will learn how to:
Identify problems that can be addressed with big data storage and processing techniques
apply the basic nosql technologies for real-world problems
Applying knowledge and understanding
Given specific project cases, students will be able to:
Define and implement a big data solution for the problem
Apply it on real datasets
Making judgements
Given specific project cases, students will be able to:
Learn how to decide which data storage and processing solution to apply and how to evaluate this decision
Communication
Students will learn to:
Write a report on a project describing and motivating the decisions taken and the results obtained
Present their work in front of their colleagues and teachers
Lifelong learning skills
Students will learn how to learn and become proficient in new data management solutions

The course content is organized in three main chapters:
1. Approaches to Big Data management
Big Data problems and dimensions
Data engineering and data science pipeline
Enterprise-scale data management
Scalability and persistency vs. volatility issues
Cross-source data integration problems and architectures
CAP theorem and implications. Non-relational distribution architectures
Evolution of transactional properties: from ACID to BASE. Modern transactional architectures
Data sharding and replication
Cloud-based, scalable data processing and computing
2. Systems and Models for Big and Unstructured Data
Graph databases
Semantic databases
Columnar databases
Document-oriented databases
Key-value databases
IR-based databases
Each category of systems is covered along 5 dimensions: (1) data model (2) (declarative vs. imperative) query languages; (3) data distribution (4) non-functional aspects (5) architectural solutions.
3. Methods for the Design of Applications
Modeling languages and methods for building unstructured data applications
Design methodology within the data engineering pipeline
Schema-less, implicit-schema, and schema-on-read approaches

Prova scritta obbligatoria, senza prove in itinere
Prova orale condizionata (a scelta del docente)
Progetto/i - Elaborato/i obbligatorio, di gruppo, senza revisioni periodiche, senza presentazione/discussione finale
Valutazione continua facoltativa (a scelta dello studente)
The exam consists of a practical project part (project work) and a written part (written exam, with possible oral discussion if deemed necessary by the instructor). Further contributions to the mark may come from continuous evaluations throughout the course, including in-presence quizzes during lessons and other interactive modalities in class.
The project work consists of solving a realistic big data engineering problem, based on real or realistic dataset publicly accessible via Web API, datasets available online, or provided by the instructors. The project work will be developed in groups throughout the semester.
The written exam is composed of a mix of theoretical questions regarding any of the course subjects, and exercises regarding the technical content and how to apply it in practice (including programming aspects). The theoretical part consists of a mix of questions with open and closed answers. The whole exam is a closed-book evaluation. A minimum score on each part is required. Oral exam may be requested by the instructor in specific cases.
Type of assessment
Description
Dublin descriptor
Written test
Theoretical questions
Exercises focusing on big data storage and processing aspects
1,4
1, 2, 3
Assessment of project artefacts
Assessment of the design and experimental work developed by students in groups
2, 3, 5
Oral presentation
Assessment of the presentation of the work developed by students in groups
Possible oral exam on the topics of the courses, upon request of the teachers
2, 3, 4, 5

Sadalage, Fowler, NoSQL Distilled: A Brief Guide to the Emerging World of Polyglot Persistence, Editore: Addison-Wesley, Anno edizione: 2012
Marting Kleppmann, Designing Data-Intensive Applications: The Big Ideas Behind Reliable, Scalable, and Maintainable Systems, Editore: O'Reilly, Anno edizione: 2017, ISBN: 978-1449373320
Marco Brambilla, Emanuele Della Valle, Andrea Tocchetti, et al., Course Notes, Anno edizione: 2021

The course focusses on the analysis of nonlinear dynamical systems, that is, the set of techniques that can be used to understand and classify the behaviour of a nonlinear model, be it a nonlinear control system, a mechanical, electrical, environmental, social model, or any other set of nonlinear differential equations.
The goal of the course is to provide an introduction to nonlinear systems theory with particular focus on bifurcation analysis. We will use common tools to classify the behavior of a system, and learn to explain how its behaviour changes as a consequence of changes in parameters or control variables. The main bifurcations occurring in nonlinear systems will be discussed in detail: they will be first illustrated on simple examples, and then on case studies with the help of software tools.

Referring to the Dublin descriptors lectures and exercise sessions will allow the students to:
•(DdD 1) know and understand the basic results of nonlinear systems theory for the characterization of a system’s solutions;
•(DdD 2) apply the basic results of nonlinear systems theory to the characterization of a system’s sensitivity to model and control parameters;
The computer laboratory will allow students to:
•(DdD 2) use the Matlab toolbox Matcont to numerically analyze and characterize the nonlinear behavior of some relatively complex real-world case studies;

Introduction to nonlinear dynamical systems
Stability and phase portrait near equilibria and fixed points
Changes of variables and topological equivalence
Nonstationary asymptotic behavior of nonlinear dynamical systems: cycles, tori, chaos, multi-stability
Parameterized systems and structural stability
Bifurcations of equilibria and cycles, robustness analysis
Transitions from periodic to aperiodic oscillations.
Introduction to numerical bifurcation analysis and continuation methods.
The course is organized into: lectures, in which the theoretical notions are presented and illustrated with the help of simple examples; exercises, in which the theoretical notions are applied; computer labs, in which the students will be introduced to the Matlab toolbox Matcont and will work in small groups analyze a relatively complex nonlinear model.
All activities will be integral part of the course program.

Prova scritta obbligatoria, senza prove in itinere
A written exam at the end of the course, in which students will be asked to:

• solve simple numerical and analytical problems regarding the solutions of a given system and their sensitivity to model and control parameters (DdD1,DdD2);
• answer theoretical questions on all topics of the course (DdD1);
• answer practical questions on the computer labs (DdD2).

The course provides fundamental notions aimed at building solid awareness of the responsibilities arising from technical activities.
Specifically, the course is oriented towards providing a general overview of the rights and duties of technical experts, along with the identification of tools and procedures for legal protection (such as insurance, contractual clauses). Furthermore, the course aims to illustrate the rules protecting inventions - copyrights, and patents - granting the student immediate access to reference sources and applicable regulations, also through the analysis of practical cases.
The last part of the course offers an analysis of the legislation related to artificial intelligence and its evolution.

The course aims to enable students to address real health and social-care challenges through interdisciplinary, project-based work carried out with external partner organizations such as third-sector entities, social enterprises, foundations, patient associations, voluntary associations and care providers. Students work in small mixed teams to transform a real need into a feasible solution, integrating biomedical, technological, service-design and management perspectives.
The course focuses on the development of a digital or physical prototype and of the related service and implementation model. Starting from the needs of users and stakeholders, each team defines requirements, explores constraints, designs and iterates a solution, and communicates the final proposal to academic tutors and external partners. The course contributes primarily to SDG 3 - Good Health and Well-being, with particular attention to access, inclusion, quality of care, autonomy and support for vulnerable users.
The learning experience is designed as a student-centred laboratory: teachers and tutors act as facilitators, while students take responsibility for planning, decision-making, teamwork, interaction with external stakeholders and delivery of project results within a defined timeframe.
Descriptions of the previous projects can be found here:
https://www.ccsbio.polimi.it/technology-for-health-and-social-challenges/

At the end of the course, students will be able to:
Analyses a real health or social challenge by identifying stakeholders, user needs, functional requirements, technical constraints, regulatory/privacy issues and implementation conditions relevant to the proposed solution (DdD 1 - Knowledge and understanding, and DdD 2 - Applying knowledge and understanding)
designs, prototypes and critically evaluates a feasible solution, integrating biomedical and management-engineering competences with methods such as need assessment, design thinking, project management, service design, business modelling and health technology assessment (DdD 2 - Applying knowledge and understanding, and DdD 3 - Making judgements)
works effectively in an interdisciplinary team, makes and justifies project decisions under time and resource constraints, incorporates feedback from tutors and external partners, and communicates project results clearly to both technical and non-technical audiences (DdD 3 - Making judgements, DdD 4 - Communication skills, and DdD 5 - Lifelong learning skills)

At the beginning of the course, students are introduced to the available challenges proposed by external organizations. After the allocation of students to interdisciplinary teams, each group is supported by academic tutors and by representatives of the partner organization.
The course covers and applies the following topics:
framing of real health and social-care needs, stakeholder mapping and user requirement analysis;
design-thinking methods, interviews or interaction with users and organizations, and translation of needs into functional requirements and technical specifications;
development of digital or physical prototypes, including iteration based on tutor and stakeholder feedback;
service design and implementation planning, including value proposition, sustainability and business model considerations;
project planning, team coordination, milestone management, risk identification and mitigation;
essential regulatory, ethical, privacy and data-management aspects relevant to health and social-care technologies;
introductory elements of health technology assessment and decision-support for the adoption of the proposed solution;
preparation of project deliverables and final public presentation of the solution, typically in collaboration with Fondazione Triulza and the participating organizations.
The specific challenge, partner organization and expected project output may vary from year to year, while the structure of the course remains centred on real-world problem solving and prototype development.

Progetto/i - Elaborato/i obbligatorio, di gruppo, con revisioni periodiche, con presentazione/discussione finale
Valutazione continua obbligatoria
The final mark is based on a group baseline assessment, subsequently adjusted through an individual assessment:
Group baseline assessment: the project work defines the reference mark for all members of the team. This assessment considers the quality of the needs analysis, requirements definition, prototype or solution developed, service/implementation model, project deliverables and final presentation to tutors and external stakeholders.
Individual assessment: the evidence of each student's contribution are used to adjust the baseline group mark. The final individual mark may therefore be increased or decreased with respect to the group evaluation, depending on the student's understanding of the project, ability to justify methods and design choices, critical judgement, communication skills, and actual contribution to the team work.
Intermediate review meetings are used as formative assessment moments: they allow students to receive feedback, revise the project direction and monitor progress, but they do not replace the final evaluation. Active participation in team work, meetings with tutors and interactions with external organizations is required.

The course aims at providing knowledge and understanding of modern concepts and methods for vibration control as well as for monitoring and diagnostics of mechanical systems. Modelling approaches and criteria for proper system analysis are introduced, together with experimental modal analysis. The main methods for vibration control are illustrated, including passive, semi-active and active strategies. Monitoring and diagnostics of mechanical system are addressed through model-based and data-driven approaches, considering signal processing and damage diagnostic methods based on vibrational response. The topics are treated both theoretically and practically, through traditional lectures and experimental/numerical labs.

With reference to the Dublin Descriptors, the course is expected to provide the students with:
Knowledge and understanding (Dublin Descriptor 1, DD1)

• a knowledge of the modelling options and criteria for the mechanical problem under study
• the knowledge and ability to apply vibration control solutions (passive, semi-active or active)
  Applying knowledge and understanding (Dublin Descriptor 2, DD2)
• the ability to set-up and use experimental tests to extract information regarding the modal parameters of a vibrating structure
• the ability to analyse experimental data for monitoring and diagnostic purposes
  Making judgements (Dublin Descriptor 3, DD3), Communication (Dublin Descriptor 4, DD4)
• the ability to critically analyse and discuss control and diagnostic solutions for vibrating systems

Vibrating systems and modelling criteria
Review of vibration analysis of discrete and one-dimensional continuous systems.
Bending vibration of thin plates (Kirchhoff theory). Vibration modes of rectangular and circular plates with different boundary conditions (analytical and finite-element numerical solutions). Fundamentals of sound radiation by vibrating structures. Modelling criteria based on the problem under study and on the frequency range of interest.
Experimental modal analysis
Overview of test procedures and excitation techniques. Impact excitation. Random and deterministic excitation. Specialized excitation techniques (pseudo-random, burst random, sine chirp and digital stepped sine).
Frequency Response Function estimation. Mathematical formulation of the FRF based on modal superposition. Frequency-domain modal parameters identification (multi-mode curve fitting method and simplified single-mode identification procedure).
Vibration control of discrete and continuous systems
Passive control. Vibration control by parameter optimization. Vibration isolation and impedance mismatch. Damping treatments, tuned mass dampers and dynamic vibration absorbers.
Semi-active control. Vibration mitigation by controllable stiffness/damping. Continuous and on-off skyhook control strategies. Smart materials technology for active structures.
Active control. Vibration control through LQR optimization, hybrid control, modal and resonant control. Input shaping for flexible systems undergoing motion in the large.
Monitoring and diagnostics of mechanical systems
Model-based and data-driven approaches. Signal processing for monitoring and diagnostic purposes. Hilbert-Huang transform. Wavelets.
Damage diagnostic methods based on vibrational response. Methods based on modal frequency, shape and damping. Curvature and flexibility method. Modal strain energy method. Cross-correlation method.

Progetto/i - Elaborato/i obbligatorio, individuale, senza revisioni periodiche, con presentazione/discussione finale
The course includes lectures, numerical and experimental labs and case studies. Students are requested to prepare short reports on the assignment given during the labs, to be delivered at the final exam. The exam consists of a discussion the report.
The exam is intended to verify:

• the knowledge of modelling options, vibration control solutions and diagnostic solutions (DdD1)
• the ability to set-up proper experimental tests, vibration control solutions and monitoring/diagnostic systems (DdD2)
• the ability to critically analyse and discuss the adopted solutions (DdD3,DdD4)