Mechatronic Engineering

The aim of the subject is to offer the student a basic knowledge of additive manufacturing technologies and materials and the relative application fields, while developing their ability to define the most suitable selection strategies on the basis of the product, of the material and of the production volume. The student will also be able to integrate AM techniques with conventional ones in order to maximize the advantages, in terms of reduction of costs and time to market. At the end of the semester, the student will be asked to: 1. Have knowledge of the AM systems available on the market, the materials associated with each technique, as well as the advantages and the drawbacks of their use; 2. Identify the most suitable AM technique to obtain a reduction in the product/process development times and costs on the basis of the typology of the product; 3. Integrate AM technologies with conventional ones; 4. Apply AM techniques to practical cases: from the conceptual model to the final production; 5. Have knowledge of specific properties of materials produced by additive manufacturing technologies; 6. Understanding of basic principles to optimize material design in additive manufacturing processes; 7. Have knowledge of strategies for optimization of mechanical properties and selection of the best material/technology choice on the basis of the final component; 8. Have knowledge of mechanisms able to control post-processing treatments for material optimization.

The subject is aimed at providing an overview of the state of the art of additive manufacturing with specific reference to processes and materials. The objective of the lectures is to offer a general introduction to additive manufacturing technologies, but also to explain the reasons behind their worldwide diffusion in various industrial sectors (such as the aerospace, automobile and white engineering sectors) and in other sectors (such as the biomedical and jewellery sectors). As concerns AM systems, the AM cycle will be analyzed, the techniques will be classified and the main application fields will be illustrated. The AM techniques that are currently available on the market will be described in detail together with their advantages and drawbacks. Fundamentals of Design for Additive Manufacturing will be supplied to the student for the development of the subject practice. As regards materials, the subject aims to supply the student with knowledge concerning materials suitable and developable through additive manufacturing technologies. Starting from the engineering background about materials acquired in the previous modules, this subject will supply the basic understanding of materials for additive manufacturing to optimize their exploitation by using these innovative technologies, also considering the design requirements of different applications.

Lectures (36 h) 1. Introduction to Additive manufacturing (AM) (3 h) • The philosophy of layer manufacturing and its economic justification; • Integration with 3D CAD systems; • Classification of AM techniques; • The economic reasons behind the diffusion of AM techniques. 2. AM techniques for the production of components of polymeric materials (4.5 h) • Stereolithography; • Polyjet/Project processes; • Direct light projection; • Drop on Demand; • Fused Deposition Modeling; • 3D Printing; • Selective laser sintering; • Continuous Filament Fabrication; • Multi Jet Fusion; • ISO G-code programming of an open 3D printer. 3. AM techniques earmarked for the production of components of metallic materials (4.5 h) • Powder bed processes (selective laser melting, electron beam melting, etc.); • Directed energy deposition (laser engineered net shaping, electron beam additive manufacturing, etc.). 4. Design for Additive Manufacturing (9 h) • Optimization of part design through Topology Optimization; • Part orientation and supporting; • Manufacturing requirements and specifications; • Integration with conventional processes for the finishing of metallic components produced by means of additive manufacturing. 5. Materials for Additive Manufacturing (18 h) • thermoset and thermoplastic polymeric materials, and their composites; • advanced metallic materials and their composites; • ceramic materials and their composites; • functional materials. For each of the above-mentioned material types, the following aspects will be analyzed: • Starting materials production; • Process effect on material characteristics; • Microstructures and properties of the produced parts; • Criteria for optimization of design parameters; • Capability of starting materials recycle and reuse in view of an LCA analysis of the process. Training (21 h) The training is carried out at the "RMLab" laboratory at the Department of Management and Production Engineering. The training will focus on the Design for Additive Manufacturing (DFAM) approach that will be applied for the definition of the shape of components in polymeric/metallic materials. One replica of the components will be fabricated by the Additive Manufacturing systems already available at the Additive Manufacturing Interdepartmental Center of the Politecnico di Torino (IAM@POLITO – http://iam.polito.it). As concerns to the materials for additive manufacturing, the training will be aimed at the presentation of testing equipment and procedures for material characterization. Moreover, the microstructures of different materials for additive manufacturing will be analysed.

Modalita di esame: Prova scritta (in aula); Elaborato scritto prodotto in gruppo;
Exam: Written test; Group essay;
... The aim of the final exam is to verify the student’s knowledge about additive manufacturing processes and materials. The final exam is composed of two parts: - a written exam about the topics covered during the lectures; - a discussion with evaluation of the technical report of the training. The written exam consists of five or six open questions and it is closed books and closed slides or notes. The duration of the written exam is about 2 hours. The written exam is aimed at the evaluation of student's knowledge about: 1) AM systems available on the market and the advantages and drawbacks of each technique; 2) specific properties of materials produced by additive manufacturing technologies; 3) basic principles for the optimization of material design in additive manufacturing processes; 4) the control of post-processing treatments for material optimization; 5) the integration of AM technologies with conventional ones; The technical report about the project that was developed in group during the training must be delivered at least a week before the beginning of the exam session. The technical report is aimed at the evaluation of student expertise through team working in the adoption of topology optimization and the application of Design for Additive Manufacturing (DfAM) guidelines for design and manufacturing of metallic and polymeric components. The written exam contributes to the definition of the final grade for a maximum of 24 points out of 30. The grade is integrated by the evaluation of the technical report for a maximum of 6 additional points. In case of maximum grade (30 over 30), the honor is attributed at the discretion of the professor.

PowerPoint slides presented during the lectures and technical papers will be provided to the students on the subject website. The following book is suggested as a basic reference for additional readings, but not strictly required: I. Gibson, D.W. Rosen, B. Stucker, "Additive Manufacturing Technologies", Springer.

By the end of this course, students will gain the following knowledge and skill: - Skill in analyzing the dynamics of discrete-time, sampled-data, digital systems by means of models in the discrete-time and/or frequency domains. - Knowledge of the digital control system requirements. - Knowledge of the basic digital control approaches and of the related design methodologies. - Skill in designing digital controller through basic methodologies. - Knowledge of the main technological and numeric problems in the sampled-data control systems. - Knowledge of advanced digital control methodologies such as adaptive control and model predictive control. - Skill in designing digital control systems by means of adaptive control and model predictive control techniques. - Skill in evaluating control performance by numerical simulation.

The course is taught in English. The course provides tools for analysis and design of digital control systems. Both standard and advanced digital control design methodologies and architectures are introduced.

• Analysis of discrete time, sampled data and digital systems (15 hr) - Control requirements, structures and architectures of digital control systems (6 hr) - Digital controller design through analytic approaches (15 hr) - Realization of digital controllers (3 hr) - Adaptive control fundamentals (6 hr) - Model Predictive Control fundamentals(15 hr)

E' possibile sostenere l?esame in anticipo rispetto all?acquisizione della frequenza

The main reference textbooks are: (1) K.J. Åström, B. Wittenmark, 'Computer-controlled systems', Prentice-Hall, 1997. (2) G.F. Franklin, J.D. Franklin and M. Workman, Digital control of dynamic systems, Addison Wesley,1997. (3) K.J. Åström, B. Wittenmark, 'Adaptive control', Addison-Wesley, 1995. (4) J.B. Rawlings, D.Q. Mayne, Model Predictive Control: Theory and Design, Nob-Hill Publishing, 2009. (5) F. Borrelli, A Bemporad, M. Morari, Predictive Control for Linear and Hybrid systems, Cambridge University Press, 2017. Lecture slides will be available on “Portale della didattica� as well as laboratory practice handouts.

Capability to understand the structure of a traction electrical drive consisting of a power electronic converter and an electrical machine. Ability to compare different electric drive solutions based on different motor technologies. Capacity to design or to tune the control loops (torque, current, speed) of an electric drive.

The course aims at providing to the students the basic concepts of the electrical energy conversion systems employed in transportation electrification, with a particular focus on hybrid and electrical vehicles. The systems under study will be the electrical drives for powertrains and the battery chargers. The electrical motors belong to the new generations of eMotors dedicated to electrical traction, such as induction motors, synchronous reluctance motors and internal permanent magnet motors. The course will provide also the basic concepts of vector control of traction drives, along with testing techniques to obtain efficiency maps and torque production maps.

• Introduction: general description of an electric drive • Power electronic devices used in power electronic converters • Basic operation principles of a switch-mode power electronic converter. Canonical commutation cell. • DC/DC converters for eMobility. • DC / AC converters (inverters) for eMobility. • Electrical motor solutions for eMobility: induction motor, synchronous reluctance motor, internal permanent magnet motor • Fundamental concepts of vector control for AC motors fed by inverters • Vector control of eDrives: design of the drive control loops (current, speed and position). • Implementation issues of high performance vector control of eMotors: Maximum Torque per Ampere (MTPA), Maximum Torque per Volt (MTPV), torque estimation, computation of maximum torque according to the voltage and current limits • Advanced testing of eDrives. • Battery chargers: on-board and off-board solutions.

Modalita di esame: Prova scritta (in aula);
Exam: Written test;
... The exam consists of a written test of 150 minutes, containing multiple choice questions (24 points) and a question with open answer (6 points). During the exam, the use of books, notes or equivalent materials is not allowed. In addition, during the teaching students will be given a report to be elaborated at home, regarding the sizing of an electric drive and the design of the its torque control scheme. The report is rated up to 30 points. The final score is calculated as the weighted arithmetic mean between the written test (80%) and the score obtained for the report (20%). The rules of examination are described and discussed during the introductory class.

• Neil Storey “Electronics: a system approach�, Pearson Education • T.L. Floyd “Electronic Devices�, Prentice Hall • Ned Mohan, Tore Undeland, Tim Robbins, "Elettronica di Potenza", Edizioni Hoepli • Rashid, M. H., 'Elettronica di Potenza', Prentice Hall • John Kassakian, Marting F. Schlecht, George V. Verghese, “Principles of Power Electronics�, Pearson Education.

• Knowledge of various types of amplifier stages and their applications; capacity for analysis and design of related circuits; selection of components considering the effects of various design choices. - Ability to analyze and design amplifiers and other circuits (active filters, circuits with positive reaction, etc...) based on operational amplifiers; knowledge and use of the choice parameters and criteria for operational amplifiers and other functional integrated modules and circuits. - Quantitative assessment of the effect of sampling and quantization in the conversion architectures from analog to digital and backward; design of A to D circuits with errors distribution between the various modules. Knowledge of various types of A to D and D to A converters, of their characteristics and related circuits; ability to choose analog integrated components parts and their use in complex circuits. Operation of A to D and D to A converters and differential assessment of the oversampling effects. - Knowledge and use of the latest generation of programmable digital FPGAs devices and criteria for the selection of embedded systems for mechatronic applications in the industrial and automotive field - Programming techniques and code generation for FPGA and DSP from simulation environments and high-level design - Knowledge of issues related with interfacing electronic components at board and system-level - Elements of system design and drafting of specific projects in complex electronic systems.

The course is taught in English. This course is mandatory for the Master of Science in Mechatronics Engineering. The program includes topics of general electronics, with particular attention to all the aspects related with the acquisition and processing systems architectures for embedded systems, analyzing the most important aspects of analog, digital and mixed signal circuits and subsystems typical of electronic applications in the automotive and industrial sectors.

Topics covered during lessons and practical laboratory activities and relative weight in credits (cr). - Amplification circuits, models and elementary stages (1cr) - Amplifiers and active filters; choice of components and practical applications (1cr) - Conversion systems A to D to A, parameters, errors, advanced circuits for ADC and DAC (2cr) - Digital architectures and programmable devices (1cr) - Programming FPGA (1cr) - Interfacing circuits (1cr) - Electronic system design (1cr)

Modalita di esame: Prova scritta (in aula); Prova orale facoltativa; Elaborato scritto prodotto in gruppo;
Exam: Written test; Optional oral exam; Group essay;
... The final exam consists of two distinct written parts that are carried out together in the same day: one part related with exercises / project analysis (such as those seen during the course and in the laboratory exercises) and a second part related with theory (three or four open questions, 5 minutes time for each question). The first part has a typical duration of about 60 minutes, while the second depends on the number of questions. The exam lasts less than two hours. During the first part (exercises), you can use notes, forms, educational materials, etc... but is NOT allowed the use of personal computers (only classic calculators). During the second part (theory), you cannot use any material and you do not need calculators. The two parts are evaluated separately and an average score is made (in thirtieths). The score obtained from the written exam is summed with the evaluation of laboratory exercises (delta max. between -2 and +4). The resulting score can be registered as it is or further integrated (between -3 and +3) with a possible optional oral session. NEW ADDITIONAL EXAM RULES: - if you reserve the exam and you cannot attend it, please unbook or send an email to communicate about your absence. If you don't do that, you will not granted to attend next exam session - you still can REJECT the score, BUT ONLY ONCE - to pass the exam, both sections MUST be greater or equal than 12/30 otherwise the exam will be FULLY FAILED

The reference text, which covers most of the topics is: J. Millman, A. Grabel, P. Land: Electronics Millman, McGraw-Hill, 2008. Copies of the presentations used in lectures, examples of written examination and exercises, as well as text and datasheets of the components used for the laboratory exercises are available. All course material is available for download through the didactic website.

The student shall acquire the following knowledge and develop the following abilities: 1) Knowledge of main methods and software tools for building mathematical models (based on physical-laws or in form of difference equations, input-state-output equations or transfer functions) of linear and nonlinear (static or dynamic) systems 2) Knowledge of main methods and software tools for evaluating estimate reliability and model quality 3) Knowledge of basic theoretical properties of main methods for building mathematical models of static or dynamic systems 4) Skill in building mathematical models of linear and nonlinear systems exploiting both physical information and experimental data 5) Skill in evaluating estimate reliability and model quality

The course is taught in English. The purpose of this course is to provide basic theoretical methodologies and software tools for building mathematical models of linear and nonlinear (static or dynamic) systems from experimental data.

Course topics and relative devoted time: - Introduction to estimation and prediction problems. Main statistical estimation methods (least squares, weighted least-squares, maximum likelihood estimators, Bayesian estimators) and their basic properties (correctness, consistency, efficiency), with evaluation of parametric estimation error (18 hours) - Set-membership estimation theory for different norm assumptions on noise, with evaluation of Estimate Uncertainty Sets and Intervals. Optimal and Central estimates, with evaluation of Feasible Parameter Sets and Parameter Uncertainty Intervals (7 hours) - Introduction to Kalman filtering problem: dynamic one-step and multi-step predictors, dynamic optimal filter, steady-state one-step predictor and filter, nonlinear predictors and filters (11 hours) - Identification of linear dynamic systems from input-output measurements: FIR, ARX, ARMAX and OE models. Predictive approach and models in predictor form. Asymptotic analysis of prediction-error identification methods. Least-squares method: probabilistic analysis, persistence of excitation, practical procedure. Recursive least-squares methods. Model structure selection and validation (whiteness test and residual analysis; FPE, AIC and MDL criteria) (18 hours) - Identification of nonlinear dynamic systems from input-output measurements: statistical and set-membership methods. Neural networks: approximation properties, learning (6 hours)

Modalita di esame: Prova scritta (in aula); Prova pratica di laboratorio; Elaborato scritto individuale;
Exam: Written test; Practical lab skills test; Individual essay;
... The final assessment consists of an individual written test, about three hours long, to be performed in the computer laboratory using the MATLAB software tools, and it is aimed at evaluating the competencies of the student with reference to all the subjects of the course program. The examination aims at verifying the knowledge and the abilities listed as items from 1) to 5) in the "Expected Learning Outcomes" section: the proposed problems not only require to choose and apply the most suitable instruments, but also make indispensable the logical concatenation of the theoretical topics investigated during the course to correctly interpret and understand the numerical results provided by the software tools. The examination is typically made of a model building practice of an unknown system starting from given data and a second exercise on Kalman predictor and/or filter design and simulation for a given linear dynamic system. The candidate has to provide a clear report that includes the reasoning behind the computations, the main numerical results and their possible critical analysis. The maximum score of the exam is 32/30; about 60 per cent of this score depends on the evaluation of the model building practice. The test is closed books; the candidate is not allowed to use textbooks or notes, except the official formulary, directly provided by the teacher during the exam as .pdf file (and downloadable before the exam from the course web page). No other material is allowed, i.e., no personal notes, exercises, portions of MATLAB code or solutions of specific exercises, in complete or partial form, anyway coded.

The following textbooks have been mainly addressed in the organization of the course: - S. Bittanti, "Teoria della Predizione e del Filtraggio", VII edition, Pitagora Editrice Bologna, 2004 (in Italian) - S. Bittanti, "Identificazione dei Modelli e Sistemi Adattativi", VI edition, Pitagora Editrice Bologna, 2004 (in Italian) - T. Kailath, A. H. Sayed, B. Hassibi, "Linear Estimation", Prentice Hall, Upper Saddle River, N.J. (U.S.A.), 2000 - L. Ljung, "System Identification: Theory for the User", II edition, Prentice Hall PTR, Upper Saddle River, N.J. (U.S.A.), 1999 - L. Ljung, "System Identification Toolbox User’s Guide", The MathWorks Inc., Natick, MA (U.S.A.), 1988-1997 On the course web page www.ladispe.polito.it/corsi/MIC/ , teaching material is available about specific issues addressed in the course, like: lecture slides, laboratory exercises with proposed solutions, official formulary.

By attending this course the student will get: Basic theory of fluid mechanics; Knowledge of main pneumatic and electro-pneumatic components and their application to automatic machines; Knowledge about operating principle of pneumatic and electro-pneumatics schemes and circuits Knowledge and ability about methods for pneumatic and electro-pneumatic circuit design. Basic knowledge on programmable logic control. Ability in wiring pneumatic and electro-pneumatic circuits and in preparing ladder programs for sequential circuits.

The course is taught in English. The teaching will provide fundamental knowledge on fluid-mechanic automation with particular reference to pneumatic actuations. Students will learn about basic components of pneumatic systems, circuit design and will be able to understand the operating principle of pneumatic schemes and circuits.

Introduction to the course. Basic principle of automatic/pneumatic systems. Organization of a pneumatic system. Unit of measure. Fluid properties. Measuring instruments. Pneumatic actuators: cylinders, specialized cylinders, regulation. Valves (directional, control, auxiliary, ...). Basic pneumatic schemes. Logic elements (pneumatic, contact-relay and logic schemes). Digital techniques for pneumatic applications. Functional and operative diagrams (step-displacement, grafcet, …). Wired logic control. Pneumatic circuit design methods: pulse limiters, auxiliary memories, sequencer. Contact-relay diagram and electro-pneumatic circuits (auxiliary memories, batch technique). Ladder diagram. Notes on programmable logic control: general features, operating principle and programming. Complementary elements and interfaces. Sensors and transducers in fluid automation. Behaviour of pneumatic valves, flow coefficients. Dynamic behaviour of pneumatic systems. Notes on modelling and simulations of pneumatic systems. Compressed air plant, air treatment. Reliability, energetic and safety aspects.

Modalita di esame: Prova scritta (in aula); Elaborato progettuale in gruppo;
Exam: Written test; Group project;
... Examination consists in a written exam and in the evaluation of the labs (group performance). In particular: 1- The written exam constists of an individual exam dealing with all topics illustrated at lessons. 2- Group performace consists in evalating the abilities and skills of each "students group" to perform all the given tasks during the labs practises. Each contribution to the exam will be marked out of 30Lode. The final mark will be the arithmetic average of the contributions above.

Lessons and Tutorials: the subject is fully treated in notes and slides provided by the teacher. Laboratory experiences: texts of experiences will be provided by the tutor. All materials will be made available on the course website, in the "materials" section. Reference textbooks. Peter Beater, Pneumatic drives – System design, Modelling and Control, Springer, Berlin, 2007 G. Belforte, Manuale di Pneumatica, II Edizione, Tecniche Nuove, Milano, 2005. Additional material will be provided during the lessons/laboratories (even through the educational portal).

The purpose of the course is to provide the basic knowledge of space missions and systems, helping the development of capabilities in formulation and solution of problems related to the design, realization and operation of space systems. Students will attain basic knowledge of the following topics: space system types, their application context, and ground support systems; technical features of on-board systems and subsystems, both in terms of operation tasks and necessary technologies. Specific objectives are knowledge of: specificities of space missions in terms of operational and functional characteristic; different types of space systems (both manned and unmanned) in orbit (satellites, probed, transfer vehicles, space stations) and on ground for planetary exploration (landers, rovers, and permanent habitats); space operational environment ad spaceflight mechanics most relevant operational orbits on board subsystems; relative dependence among mission requirements/constraints and features of the space systems, and relations at the system-subsystem lavel;

The main objective of the course is to contribute to the education of the mechatronic engineer, providing the basic knowledge on missions and space systems that represent an application field of relevant interest for the most innovative solutions of mechatronic engineering. The course offers a path aimed at learning knowledge and skills training in the field of Space Systems, i.e. complex innovative systems featuring novel technology, integrated within modern engineering systems for transport and telecommunications, and used for societal services like navigation, remote sensing and monitoring, and Internet of Things. The course therefore aims at developing in the student a multidisciplinary and critical mindset, providing the basic knowledge and tools necessary to understand Space Missions and the Systems that carry them out. Modern Space Systems are characterized by a high degree of complexity, mainly due to the multiple relationships and interactions between the elements that constitute the system itself and the different systems that contribute to the realization of the Space Mission. The skills acquired throughout the course are functional to support and encourage the student's inclusion in a high-profile professional context, in industry and in research centres in the aerospace domain and related fields, such as the automotive industry and robotics in general.

Space systems Introduction to the concept of system as integration of several systems and elements. System engineering as an approach and method for the design and operation of space systems and missions (3h). Introduction to the application context od space systems: challenges and opportunities of space exploration and exploitation(1.5h) Space environment and ots effect on space systems (3h). Type of space systems in the gereral context of space missions: orbital systems (satellites, probes, transport vehicles)m surface systems(landers, rovers), launchers, ground stations (4.5h). Payloads (3h). Electric power generation, storage, conditioning and distribution: electric power system (3h). Thermal protection and control (3h). Telecommunication system (3h). Data and command handling (3h) Astrodynamics principles of spaceflight mechanics: two-body problem, angular momentum and energy of orbits, keplerian orbits. orbital elements, reference systems, and ground track (5h). Geocentric orbits: classification, orbital transfers with impulses, orbital perturbations and their effect of orbital elements (5h). Relative motion and GNC: relative motion equations, exaples of proximity maneuvers, rendezvous and docking.equazioni del moto relativo tra due satelliti in orbita, esempi di manovre di prossimità , rendezvous and docking, strategie di Guidance, Navigation and Control (GNC)(5h). Rigid body rotational dynamics: Euler equations for rotational motion, Euler's angles and quaternions, torque-free motion and active attitude control (5h). Propulsion system Principles of space propulsion, thrust and specific impulse (3 h). Maneuvers, structural loads during launch (4.5 h). Chemical propulsion and electric propulsion: thrusters and operations, subsystems (7.5 h). Feed systems (3 h).

Modalita di esame: Prova scritta (in aula);
Exam: Written test;
... The exam is written, with multiple-choice questions and open-ended questions. The length is 1.5 hours. Maximum mark 30/30.

Cornelisse, J.W., Schoyer, H.F.R., and Wakker, K.F., Rocket Propulsion and Spaceflight Dynamics, Pitman, London, 1979.

Students will acquire knowledge on wireless distributed network systems and the know-how to effectively apply these technologies to mobility services, system and environmental monitoring and data collection. In particular, students will develop competences on the medium access layer and the network layer of distributed wireless systems, and the ability to design a wireless network given the constraints that typically characterize practical smart city scenarios. In more detail, students will acquire knowledge and ability as reported below: 1. Knowledge of mobile network systems based on device-to-device communications and of sensor network applications. 2. Knowledge of protocols for radio channel access and traffic routing in distributed wireless networks. 3. Knowledge of channel access techniques, adaptive topology formation and traffic routing in wireless sensor networks. 4. Knowledge of the main performance metrics, which are relevant in wireless communication-based systems. 5. Ability to design a channel access protocol for wireless networks. 6. Ability to design a routing protocol for wireless networks under practical constraints and in relevant scenarios. 7. Ability to evaluate the performance of a distributed network system. 8. Ability to assess the suitability of a wireless technology to a given environment for application support.

The course introduces students to the fundamental aspects of communication networks for mobile users and of sensor networks, which represent the main ways to disseminate and collect information in smart cities, smart buildings, health care centers as well as for applications such as environmental monitoring. The course objectives are therefore to let students acquire (i) the necessary knowledge on wireless and mobile communication technologies, and (ii) the ability to apply such knowledge to the support of services for smart cities. Special attention will be given to WiFi-based technologies, device-to-device (or machine-to-machine) communication and to wireless sensor networks. For each application scenario (smart city, health, environmental monitoring), the main network technologies are discussed, along with their performance and the open problems for which a solution is still needed. The students will also learn the methodologies that can be used for modeling such systems and the main results that have been obtained. The knowledge and abilities that students will acquire through this course are relevant to create experts in the design and management of network services, and in the development for services in application domains such as transport, environmental monitoring, domotics, health.

The course includes both lectures and numerical exercises, which focus on wireless distributed systems. In particular, they describe the main existing and emerging technologies and highlight their features and challenges in different application scenarios. The topics covered by the course, and their weight expressed in hours, are as follows: - Mobile networks and device-to-device communication: generalities, classification and main issues (6 h) - Channel access techniques in distributed wireless networks (12 h) - Connected cars: services, network architecture, channel access techniques and main technical challenges (6h). - Performance of distributed wireless networks (4 h) - Proactive and reactive routing protocols for mobile networks (7 h) - The Bluetooth technology (4 h) - Sensor networks: generalities and applications; channel access techniques (5 h) - Network topology control and traffic routing for sensor networks (4 h). Numerical exercises and software demonstrations account for 12 h.

Modalita di esame: Prova scritta (in aula); Elaborato scritto prodotto in gruppo;
Exam: Written test; Group essay;
... The exam consists of a written test, which lasts about 1 hour and a half. No oral exam is foreseen. It is a closed-book exam, i.e., students cannot use textbooks, copy of the slides, or copy of the solutions of numerical problems. The test focuses on all course topics, specifically: WiFi/WiFi Direct, device-to-device networks, car-to-car communications, sensor networks, access control, topology formation and traffic routing. The test typically includes 4 questions, among which there is at least one numerical problem to solve. The remaining questions require the description and the analysis of system aspects. The descriptive questions aim at assessing the students' knowledge on mobile distributed network systems, channel access techniques, topology adaptation, and traffic routing mechanisms, as well as their understanding of protocols and algorithms to be used and configured in specific scenarios. Importantly, the test also aims to evaluate the students' ability to select a technological solution depending on the different practical conditions and applications. The numerical problems aim at verifying the competences acquired by the students on distributed channel access, sensor networks and traffic routing schemes, as well as their ability to correctly configure a network system. Each question is assigned a number of points, which reflects the level of difficulty of the question/problem. The answers to open questions are evaluated considering their correctness, the level of knowledge that the student has acquired on the topic, and the student’s ability to precisely answer the question and to clearly communicate the technical material with accurate terms. The solution of numerical problems is evaluated based on its correctness and technical rigor, on the rational followed by the student and on the student’s ability to apply the acquired know-how. The final mark is computed by summing the marks obtained in each question, with the final mark being 30/30 cum lode; the minimum mark students have to achieve in order to pass the exam is 18/30.

The teaching material consists of copy of the slides e during the course, text of numerical problems, and suggested reading. All the material is available on the web portal of the course. Useful references are as follows: - M. Gast, ed. (2002): "802.11 Wireless Networks: The Definitive Guide," O'Reilly (Networking), ISBN 978-0596001834. - M. Felegyhazi, J.-P. Hubaux, "Game Theory in Wireless Networks: A Tutorial," IEEE Communications Magazine. - C.S. Raghavendra, K.M. Krishna, M. Sivalingam, T. Znati, Wireless Sensor Networks, Kluwer Academic Publisher, 2004.

The student will master the concept of model-based software design, and will acquire knowledge on important standards as ISO26262, and advanced software architectures as AUTOSAR. In particular the expect results are: 1. knowledge of the ISO26262 design process; 2. knowledge of the model-based software design principle; 3. knowledge of the concept of automatic code generation; 4. knowledge of the concept of software integration into hardware platform; 5. knowledge of the concept of software validation using model-in-the-loop and hardware-in-the-loop testing; 6. knowledge of AUTOSAR.

Optional course for the master degree in computer engineering and mandatory course for mechatronic engineering. The purpose of the course is to provide a system-level view of the design of embedded software using a model-driven approach, where designers focus most of their efforts in defining what the software shall do rather than spending time in how the software behaviour is implemented. Several examples will be presented referring to the automotive industry, nevertheless, the methodology is widely adopted also in other industries such as aerospace and biomedical. A reference embedded system for a representative automotive application will be taken as example through the whole class, and different elements will be considered to address the design of such an application. The automotive international standard ISO26262 will be taken as reference for the development process, and the design and validation techniques needed to satisfy the standard will be considered. The concept of model-based software design will then be presented, with particular emphasis on the automatic software generation from behavioural/structural models. The validation process will be detailed considering both model-in-the-loop simulation, and hardware-in-the-loop validation. Finally the concepts related to automatic code generation and integration of the generated code into an hardware platform will be addressed. A short introduction to AUTOSAR standard will also be analyzed.

1. Introduction to embedded systems for mechatronics application, the hardware and the software perspectives (0.5 CFU) 2. Modern embedded system design flow, and the automotive case with ISO26262 standard. (0.5 CFU) 3. Model-based design principles and supporting technologies (0.5 CFU) 4. Guidelines for effective modelling (1.0 CFU) 5. Automatic Code generation and deployment on target hardware (0.5 CFU) 6. Validation techniques based on simulation and on static analysis (1.0 CFU) 7. Software testing techniques (0.5 CFU) 8. AUTOSAR. (0.5 CFU) 9. Laboratory. (1 CFU)

Modalita di esame: Prova scritta (in aula);
Exam: Written test;
... Written exam The written exam is composed of 6 questions, closed books, with maximum score 33. Each question is either a open-answer question where the student is asked to illustrate one of the topics of the class, or an exercise where the student is asked to apply the theory presented in class on a numerical exercise. Assignment Students shall implement a group assignment during the semester (two students per group), collecting and documenting the experiences done in each laboratory session. The assignment will be evaluated for a maximum score of 33. The final grade will be given by combining 50% of the exam score and 50% of the assignment score. In case of final score of 33 the final grade will be 30L, in case the final score is between 30 and 32, the final grade will be 30. In case of final score less than 30, the final grade will be equal to the final score. In case of score less than 18, the exam will be considered as failed. The assignment score lasts for 1 academic year.

Compulsory reading material: Lecture notes provided by the teacher. MATLAB/Simulink/StateFlow user manual Additional 0rReading material: P. Koopan, Better Embedded System Software, Drumnadrochit Education, ISBN-13: 978-0984449002 R. Bosch, Bosch Automotive Electrics and Automotive Electronics: Systems and Components, Networking and Hybrid Drive, Vieweg + Teubner Verlag, ISBN-13: 978-3658017835 K. Reif, Automotive Mechatronics: Automotive Networking, Driving Stability Systems, Electronics, Vieweg + Teubner Verlag, ISBN-13: 978-3658039745

The student acquires the following knowledge: -- How to describe motion of a rigid body in the space and related mathematical tools: vectors, matrices, fixed and mobile reference systems, translation and rotation matrices, Euler angles, quaternions; -- How to introduce generalized coordinates and compute positions, attitudes, linear and angular velocities of rigid bodies as functions of generalized coordinates (direct/inverse kinematics); -- Lagrangian approach to dynamics: kinetic and potential energy, Rayley dissipation function, virual work; -- How to transform dynamical equations into the state-space form and linearize them; -- How to describe the structure of a system; various types of analytical representation (linear, non-linear, time invariant, time-varying, etc.) and their representations based on physical principles; - The concepts of flows and efforts; power and the bond-graph methodology; The student develops the following abilities: - Ability to describe a rigid body in space and its motion using vectors and matrices (rotation and homogeneous); - Ability to compute analytically and numerically the kinetic and potential energy of a system that includes many bodies (multi-body analysis); - Ability to write the Lagrange equation, to transform in state variable equations and use for model simulation; - Ability to write the Bond-Graph model, to transform in state variable equations and use for model simulation; - Ability to use the above methods for modeling mechanical, electromagnetic and electromechanical systems such that actuators and transducer (electromagnets, capacitive, moving-coil transducers, piezoelectric material, etc.).

To predict when the engine of our car needs maintenance, to design an algorithm that will land a spacecraft on the Moon, and to build a realistic digital twin of a smart factory we need a computer model of the system. The heart of every computer model is a mathematical model, that is, a system of mathematical equations (state equations) that governs the system's dynamics. Modern mechatronic systems are very complex and comprise components of different nature (mechanical, electromagnetic, hydraulic etc.) However, general methods exist that allow to derive mathematical models. This course introduces two approaches to complex systems modeling. The first one is based on Lagrangian formalism (generalized coordinates, energy functions, Lagrangian equations of motion), and the second one is based on bond graph diagrams (components, ports, effort and flow variables).

1. Introduction, examples of mechanical, electrical, hydraulic, electromechanical equipment, definition of power, energy and co-energy. (8 hours) 2. Analytical models: understanding of the system structure, of the analytical representation (linear, nonlinear, time invariant, time varying, etc.) and its representation based on physical principles. (10 hours) 3. Definition of the effort and flux variables in mechanical, electrical and hydraulic systems. (8 hours) 4. State functions: kinetic co-energy and potential energy. (10 hours) 5. Mathematical language needed in the course: vectors and matrices, rigid body kinematics and dynamics, generalized variables, degrees of freedom, constraints, holonomicity. Roto-translations of rigid bodies. general properties of the body dynamics: inertia, dissipation, elasticity. (8 hours) 6. Lagrange Equations. (8 hours) 7. Bond-graph modelling based on power exchange between subsystems. Power variables, reversibility, construction rules, relation between bond-graphs and block diagrams. (12 hours) 8. Configuration space and state equations. The dynamic system as a unifying mathematical representation. Linear and nonlinear models, Fundamental properties: stability and passivity. (8 hours) 9. How to write the equations that describe the interactions inside electromechanical actuators and transducer, as electromagnets, capacitive transducers, moving coil transducers, piezoelectric materials, synchronous motors. (10 hours) 10. Mechanical models of the rigid body, finite elements models. Finite elements models of lumped parameters electrical and electronic systems. Modal reduction techniques and bond-graph representation. (10 hours) 11. Examples and implementation of simulated systems. (8 hours)

Modalita di esame: Prova scritta (in aula);
Exam: Written test;
... The duration of exam is 2hours 20minutes. The exam includes 8 problems (which may be concerned with knowledge of theory, numerical computations, drawing of Bond graph diagrams, deriving of state-space equations etc.) -- 5 multiple-choice problems related to Lagrange equations (2 points in each problem, no penalty for wrong answers); -- 5 multiple-choice problem related to bond graph diagrams (2 points in each problem, no penalty for wrong answers); -- 1 open question, Lagrange equations (up to 6 points, partial grading may apply); -- 1 open question, bond graph diagrams (up to 6 points, partial grading may apply). The maximal mark is 5*2+2*6=32 (marks strictly greater than 30 are registered as 30 e lode). The exam is paper-based and closed-books. Students are allowed to bring ONE A4 PAPER as a formulary. The use of notes, books, or electronic devices is not permitted.

For the Lagrange approach: B. Bona, "Dynamic Modelling of mechatronic Systems", CELID, 2013 For the Bond-graph approach : Dean C. Karnopp, Donald L. Margolis, Ronald C. Rosenberg, "System dynamics: modeling and simulation of mechatronic systems", New York, Wiley, 2000. Slides, notes and other written material is available through the course web page

Risultati di apprendimento attesi Students who complete this course will acquire the following knowledge: - Classification of industrial robots according to their kinematic configuration - Forward and inverse kinematics of industrial manipulators, in different formulations - Velocity kinematics, Jacobian, kineto-static relationships - Forward and inverse dynamical equations and their characterization and interpretation - Decentralized and inverse-dynamics control of industrial robots - Classification and characterization of mobile and wheeled robots - Wheel characteristics and non-holonomic constraints - Kinematics of wheeled robots, kinematic control - Basic concepts on dead reckoning and path planning - Sensors and computer vision for robotics - Software simulation tools for robotics. Matlab/Simulink and the Robot Operating System (ROS) Students who complete this course will acquire the following abilities: - Analyze a kinematic chain, setting the body reference systems, and write the direct and inverse position kinematic equations - Write the direct and inverse velocity kinematic equations of a given open chain structure; analytical and geometrical Jacobian; kineto-static relations - Write and characterize the dynamical model of simple industrial manipulators using the Lagrange formulation, assessing the relative influence of the different terms in the dynamical equations - Devise independent-joint and inverse-dynamics control architectures, and assess and compare their performance - Write the kinematics of basic wheeled robotics structure - Implement basic kinematic control schemes for mobile robots - Operate a basic selection of onboard sensors, based on their characteristics and technical specifications - Simulate industrial and mobile robot behavior using the software tools studied in the theoretical sections

The course is entirely taught in English. The students who complete this course will gain knowledge on fundamental concepts in robotics, dealing with both industrial and mobile robots. Industrial robots will be studied both from a kinematic and dynamical point of view. Independent-joint and inverse-dynamics control algorithms will be introduced, as well as fundamental algorithms for the generation of reference trajectories. Mobile, wheeled robots will be studied from a kinematic point of view, and algorithms for locomotion control and path planning will be defined and studied. Elements of sensors and computer vision for robotics will complement the knowledge base of this course.

Programma - Course introduction, history of robotics and milestones - Definitions, kinematic chains, degrees of freedom, redundancy, robotics arms classification, robotic wrists classification - Direct kinematic problem, Denavit-Hartenberg convention, inverse kinematic problem - Velocity kinematics, geometrical and analytical Jacobians, statics, kineto-static dual relations - Dynamics equations, structural properties, characteristics, interpretation - Model of the motor-gearbox-arm chain, state variables, nonlinear state variable equations - Trajectory planning, methods for attitude planning, attitude planning. Practical issues, motor constraints, micro-macro interpolation - Control schemes for rigid manipulators: independent joint control and inverse-dynamics control, PD control with gravity compensation, computed torque control, interaction control - Mobile robots, wheel and wheeled robot classifications, non-holonomic constraints - Kinematics of simple wheeled structures: differential drive, unicycle, bicycle, kinematic control - Sensors and computer vision for robotics - Basic elements of dead reckoning and path planning - Simulation tools for robotics: Matlab/Simulink and the Robot Operating System (ROS) - Conclusive remarks and exam simulation

Modalita di esame: Test informatizzato in laboratorio; Prova scritta (in aula); Elaborato progettuale in gruppo;
Exam: Computer lab-based test; Written test; Group project;
... Specific learning outcomes are assessed through the administration of a final written exam. Knowledge items will be assessed through the administration of theoretical exercises and problems concerning the theoretical foundations of robotic systems. Abilities will be assessed through the administration of numerical exercises and problems, as well as through the design, analysis, or modification of software elements, written in the programming languages studied in class. The exam consists of a written test, aimed at assessing the comprehension of the theoretical concepts and the ability to apply them to practical case studies. The test consists of two sections: - the first section comprises 10 to 15 multiple-choice questions; - the second section comprises 3 to 5 problems. Such problems can be open-answer questions, numerical problems, coding problems. Incentives and penalties on the final marks may be given to students, in an amount not greater than the +/-10% (+/-3 pts), based on the active participation to the laboratory activities. Students abroad for study reasons and other particular cases will be evaluated on a case-by-case basis. The exam is closed-books, closed-notes. Students are allowed to take with them a single A4 sheet, compiled on both sides with notes and formulae at their wish. The exam duration is 2 hours. Students who would like to gain more practical insights on the topics covered in the course, may propose to the instructor the realization of a project. A project must be executed by a team of 3 to 5 students. Project topics will have to be agreed upon and approved by the instructor. The project outcomes must be presented to the instructor in the agreed form, generally through either a report or a presentation. Individual contributions must be clearly stated and identifiable. Projects are not mandatory, they do not substitute any part of the written test, and will be evaluated up to an additional 10% of the full marks (3 pts). To pass the exam, students must attain a full sufficient mark (i.e. 18/30) at the written test. This implies that sufficiency cannot be achieved thanks to incentives for laboratory or projects. To attain full grade with honors (lode), students must achieve a total of at least 33/30, including incentives. An additional oral examination may only be requested by the instructor, in order to better assess the student’s preparation.

Handouts, notes, and program codes will be provided during the course. The instructor notes will include solved and unsolved exercises. Textbooks are not mandatory. Selected parts may be useful to understand more in-depth the concepts reported in handouts and notes. 1. B. Siciliano, L. Sciavicco, L. Villani, G. Oriolo, “Robotics: modelling, planning and control�, Springer 2. M.W. Spong, S. Hutchinson, M. Vidyasagar, “Robot Modeling and Control�, Wiley 3. R. Siegwart, I.R. Nourbakhsh, D. Scaramuzza, “Introduction to autonomous mobile robots�, The MIT Press 4. P. Corke, “Robotics, Vision, and Control – Fundamental algorithms in Matlab�, Springer

Students who complete this course will acquire the following knowledge: - Understanding the main components, structures and layouts of automated production plants, as well as of stocking areas and transportation systems. - Understanding the main metrics used to evaluate the performance of production systems, e.g., cycle time, production rate, availability, etc. - Understanding the issues connected to the selection of industrial robots components for applications, such as grippers or robotic cells. - Understanding the structure of most common CNC machines, with the use of CAD-CAM programming and their related G-code simulations. - Understanding the PLC architecture and the main programming techniques for automation applications. - Understanding statistical methods and numerical algorithms to assess the performance of queuing systems. - Understanding the main methods for the numerical solution of the most common production scheduling problems: single machine, parallel machines, flow shop, and job shop. - Understanding methods and algorithms for production planning: resource allocation, assignment, distribution. Students who complete this course will acquire the following abilities: - Acquiring the terminology related to modern production systems. - Assessing the performance indices of production systems, devising strategies identify factors that limit productivity and designing method for performance improvement and optimization. - Analyzing the performance of robot components for applications, such as grippers or robotic cells. - Interpreting and modifying machining codes for CNC machines, using simple CAD-CAM software tools to design workpiece machining. - Designing and implementing simple programs for PLCs, able to tackle real-world applications. - Modeling sections of production systems through queueing systems, assessing their performance, and identifying bottlenecks and margins for performance improvement. - Modeling scheduling problems for different production systems, and solving them, finding optimal or suboptimal solutions, through heuristic or linear programming approaches.

The course is entirely taught in English The students who complete this course will gain knowledge on fundamental concepts in modeling, control, and optimization of industrial production systems. Several components of actual production systems will be analyzed, and software tools for their modeling, control, and optimization, will be introduced. At the end of this course, students will gain knowledge on the role and working principles of the main components of modern production systems, as well as on the factors that determine their performance. Students will also be able to identify key issues in modeling and optimization of such systems, as well as to use software tools for modeling, simulation, and optimization. Each theoretical section will be complemented by laboratory experiences to elucidate practical issues and let students familiarize with software tools for modeling, control, and optimization of the systems under exam.

• Overview of manufacturing systems, manufacturing operations, manufacturing metrics and economics. - CNC Machines overview, introduction to programming with G-codes. Use of CAD-CAM based simulators for the generation of the G-codes. - Complementary topics in robotics for industry; e.g., selection of grippers, performance assessment of robotic cells, use of simulation tools to simulate robots (e.g., the Matlab Robotics Toolbox by Peter Corke). - Overview of material handling components. - Simulation of industrial production systems and their control in real time with PLCs. - Queuing theory applied to detect accumulation and bottleneck nodes of a production plant. Algorithms and statistical methods for reliability evaluation and quality control monitoring of production lots. - The main scheduling problems of automated production systems: single machine, parallel machines, flow shop and job shop. Solutions obtained by known rules when applicable or, in general, by numerical algorithms. - Methods and algorithms for production planning: resource allocation, assignment and distribution. - Conclusive remarks and exam simulation

E' possibile sostenere l?esame in anticipo rispetto all?acquisizione della frequenza

Handouts, notes, and program codes will be provided during the course. The instructor notes will include solved and unsolved exercises. Textbooks are not mandatory. Selected parts may be useful to understand more in-depth the concepts reported in handouts and notes. Texbooks 1. M.P. Groover, “Automation, Production Systems, and Computer-Integrated Manufacturing�, Pearson 2. H. Jack, “Automating Manufacturing Systems with PLCs�, free download at http://claymore.engineer.gvsu.edu/~jackh/books.html 3. P. Monkman et al., “Robot Grippers�, Wiley 4. C.G. Cassandras, S. Lafortune, “Indtroduction to Discrete Event Systems�, Springer 5. M.L. Pinedo, “Scheduling – Theory, Algorithms, and Systems�, Springer

• Learning of the vehicle subsystems: chassis, engine, driveline - Learning of detailed and simplified vehicle modelling for each subsystem - Learning of vehicle control aspects and objectives for each subsystem and their interactions - Learning of possible solutions to each control problem - Ability to adapt vehicle model complexity to simulation, estimation, design aims - Ability to formulate vehicle control objectives - Ability to tune control systems and to evaluate the obtained performances - Ability to tune and evaluate a control system by means of the numerical simulation

The course aim is to provide an overview on the main automotive control problems and related solutions, dealing with chassis, engine and driveline subsystems. Some of the vehicle control systems, selected from the most common and usually present on commercial cars are deepened.

Course presentation and overview on automotive control problems (2 hours). Chassis control problems: - Longitudinal dynamic during braking and acceleration: ABS and TC (7 hours). - Yaw dynamic in steering manoeuvres: ESP (7 hours). Engine control problems: - Thermodynamic phenomena (4 hours). - Combustion engines principles (4 hours). - Air fuel ratio control (6 hours). - Idle speed control (4 hours). - Knock control (5 hours). - Cylinder balancing (2 hours). Driveline control problems: - Gear shifting (4 hours). If possible, presentation of automotive companies are scheduled during the course.

Modalita di esame: Prova scritta (in aula);
Exam: Written test;
... The exam aims at verify - the comprehension of the topics presented during the course and of the software tools used in the laboratories and the consequent strengthened analysis made on the case studies; - the ability to extend the given concepts to cases similar to the presented ones. The exam is a written examination lasting about 2 hours and divided in two Sections: First Section (laboratory on-line procedure) made of multiple choice questions (50% of total mark) with a penalty of ¼ of the question score if the given answer is wrong; Second Section made of free response questions (50% of total mark). A minimum mark of 5/16 in the First Section is mandatory to be admitted to the Second Section of the exam. The closed-answer questions deal with methodology, numerical exercises, software laboratory exercises and CarSim knowledge, etc. The open-answer questions deal with methodology and CarSim knowledge. During the exam, both Sections, it is possible to look through books, personal notes, lecture slides, etc. Optionally, studying and discussion of a homework, developed in team, about topics inherent the course can substitute the free response questions and so fulfil the Second Section of the exam (50% of total mark). The topic of the homework must be agreed with the professor and developed during the course (October-January). No written report is required, but an oral presentation given by the whole team is necessary and must be taken during the winter exam session.

U. Kiencke, L. Nielsen, Automotive Control Systems: For Engine, Driveline and Vehicle, Springer-Verlag, Second Edition, 2005. A.G. Ulsoy, H. Peng and M. Çakmakcý, Automotive Control Systems, Cambridge University Press, 2012 Additional material, such as notes, lecture slides and laboratory exercise files, is made available to students.

To give students the tools and training to recognize convex optimization problems that arise in engineering and other branches of science; to present the basic theory of such problems, concentrating on results that are useful in computation; to give students an understanding of how such problems are solved, and some experience in solving them.

Optimization is a technology that can be used for devising effective decisions or predictions in a variety of contexts, ranging from production planning, to engineering design, finance, machine learning and data science, to mention just a few. In simplified terms, the process for reaching a decision starts with a phase of construction of a suitable mathematical model for a concrete problem, followed by a phase where the model is solved by means of suitable numerical algorithms. An optimization model typically requires the specification of a quantitative objective criterion of goodness for our decision, which we wish to maximize (or, alternatively, a criterion of cost, which we wish to minimize), as well as the specification of constraints, representing the physical limits of our decision actions, budgets on resources, design requirements than need be met, etc. An optimal design is one who gives the best possible objective value, while satisfying all problem constraints. While a generic optimization approach typically leads to computational problems that are very hard to solve in practice, convex problems, which are the core of this course, possess special properties that make them amenable to very efficient numerical solution techniques. This course therefore concentrates on recognizing, modeling and solving convex optimization problems that arise in several scientific contexts (engineering, finance, control, data science, etc.), and in showing the relevance of these methodologies in practical applications.

Introduction, convex sets and convex functions. Optimization problems in standard form, optimality criteria. Systems of linear equations, Least Squares (LS), Linear Programming (LP), Ell-one norm optimization, Chebychev approximation. Application examples: generation of force/torque via thrusters, uniform illumination of patch surfaces, etc. Quadratic Programming (QP) and Second Order Cone Programming (SOCP). Application examples (e.g. FIR filter design, antenna array design, sidelobe level minimization, etc.). Linear Matrix Inequalities (LMI) and semidefinite programming (SDP). Geometric programming (GP). Software tools (CVX and/or YALMIP). Applications: data-fitting, approximation and estimation, uncertain and robust Least Squares, portfolio optimization. Geometrical problems: containment of poyhedra, classification, Lowner-John ellipsoids, linear discrimination, support vector machines. Focus topics (mutually exclusive, to be offered alternatively over years): - LMIs in systems and control theory. - Sparse optimization and compressed sensing. - Convex optimization in Finance. - Convex optimization in algebraic geometry, global polynominal positivity (positivstellensaatz). - Network optimization, distributed optimization.

Modalita di esame: Prova scritta (in aula);
Exam: Written test;
... The final exam consists in a written test, which will contain a mixture of methodological questions and numerical exercises (to be executed with pen and paper; use of a calculator is allowed) and which will take the form of multiple-choice questionar. The final score will be computed as a weighted average of the written exam score (70%) and a score counting the presence and participation to the lab sessions (30%).

G.C. Calafiore and L. El Ghaoui, Optimization Models, Cambridge Univ. Press, 2014. S. Boyd and L. Vandenberghe; Convex Optimization, Cambridge Univ. Press, 2004.

Students are required to learn the basics of the dynamics of vibration and of the analytical and numerical methods commonly used for machine design. Students must learn how to apply this knowledge to the actual dynamic study of machines and their elements, using them in a machine design context. The ability of interpreting in a critical way the results obtained, in particular through numerical methods, is also required. Student must also learn to produce technical documentation of the work done.

The aim of the subject is to give the students the basic knowledge needed for the structural dynamic analysis and the dynamic design of machines. Computational methods, and more in detail the numerical methods more common in the design practice will be intorduced. Theoretical aspects needed to obtain the deeper knowledge of the subject required to operate in the present innovative industrial environment are not neglected. The last part of the course is dedicated to the study of the dynamic analysis of rotating machinery, dynamic behaviour of electromagnetic controlled systems.

Here below is reported the Course Syllabus. Introduction to the course (2 hours). Mechanical design, static and dynamic stress analysis. Classical and numerical approach. Automatic computation in design. Numerical simulation. Computer aided engineering (CAE). Part 1 (9 hours of theory lectures, 6 hours of classroom exercise lectures). Overview on the dynamic analysis of linear systems. Discrete linear systems: equations of motion in the configuration space; equations in Lagrange form. State space. Block diagrams. Free behaviour of single and multi-d.o.f. systems. Modal uncoupling; modal participation factors. Forced response to harmonic excitation. Viscous, Viscoelastic, Electromagnetic, Structural damping. Systems with frequency dependent parameters. Forced response to non harmonic excitation; short account of random vibrations. Exercises addressing specific issues on the subject. Assignment of a project work dedicated to the fatigue design of mechanical parts in a mechanical subsystem affected by the vibration motion of the same subsystem. Part 2 (18 hours of theory lectures, 9 hours of classroom exercise lectures, 3 hours of laboratory experience). Numerical methods and discretization techniques. Lumped parameter methods, Finite element method in dynamics. Reduction techniques. Time domain and frequency domain solutions, numerical simulation. Specific exercises addressing the issues on the subject. Assignment of a project work dedicated to the design of a dynamic damper of a vibrating structure. Laboratory experience on the identification of the parameters of a vibrating structure. Part 3 (9 hours of lectures, 6 hours of exercises). Dynamics of rotating machines. Vibrations of rotors: Campbell diagram, critical speeds and fields of instability. Undamped and damped Jeffcott rotor. Rotor with 4 degrees of freedom, gyroscopic effect. Rotors with many degrees of freedom. Nonisotropic machines. Rotors on rolling, hydrodynamic and magnetic bearings. Balancing of rotors. Specific exercises addressing the issues on the subject. Part 4 (elective 7,5 hours of lectures, 3 hours of laboratory exercises). Short Outline in Controlled Electromagnetic and Electromechanical Systems: general considerations, open-loop control, closed-loop control, basic control laws, design of controlled systems. Specific exercises addressing the issues on the subject. Laboratory experience on the tuning of the control parameters of active magnetic bearings.

Modalita di esame: Prova scritta (in aula); Prova orale obbligatoria; Elaborato scritto prodotto in gruppo;
Exam: Written test; Compulsory oral exam; Group essay;
... The assessment will include a written test followed by an oral examination. To be allowed to the oral exam the student must have passed the written test with at least 18/30 rating. Written test The written test will consist of 30 multiple choice tests (correct answer: 1 point; no answer 0 points; wrong answer: -0.5 points) plus 8 exercises (correct answer: 2 points; no or wrong answer: 0 points) to be answered in 2 hours. At least 15 tests and 4 exercises must be answered. No books, notes or other material are allowed at the written test. Use of a cell phone or other communication device will cause immediate expulsion. Oral exam If the rating of the written test is between 18/30 and 23/30 (included) the oral exam may be substituted by a discussion on the project report. This can produce an increase or a decrease of the rating up to 2 points, depending on how the project are made. A complete failure of answering questions about the project report causes a failure of the exam. If the student aims to obtain more than 23/30 or he has answered less than 15 tests and 4 exercises the oral exam is compulsory. If the rating of the written test is in between 24/30 and 30/30 the oral exam is compulsory. The oral exam includes at any rate a discussion on the project reports. A failure of answering the questions on the project reports or the presentation of incomplete projects causes a complete failure of the exam. It is recommended to book the exam only when there is a reasonable expectation to actually giving the exam. The exam rules stated for the past years will apply to students who have followed the course in the past.

The textbook for the subject is: Genta G., Vibration Dynamics and Control, Springer, New York, 2009, ISBN 978 0 387 79579 9 or, alternatively, , Genta G., Vibrazioni delle strutture e delle macchine, Levrotto e Bella, Torino, 1996. Other textbooks that can be used for specific parts of the course are Genta G, Vibration of structures and machines, III ed., Springer, New York, 1998, ISBN: 0 387 98506 9 and Genta G., Dynamics of Rotating Systems, Springer, New York, 2005 ISBN: 0-387-20936-0. Additional material for exercises will be supplied through the subject website.

Two primary outcomes are expected to be achieved after attending the course. 1) Theoretical skills - Dynamic models of ac motors used in traction applications - Flux, torque and losses maps of ac motors - Parameters identification procedures of ac motors - Benchmark torque controllers for traction ac motors - Simulation models of electric machines and power electronics converters 2) Application skills - Performance evaluation of ac motors - Design of high-efficient torque controllers for ac motors - Discrete-time implementation of motor control algorithms - Advanced simulation of electric motor drives

The course aims to provide methods and development tools to design advanced torque controllers for electric motors used in traction applications (e.g., hybrid and electric vehicles). The step-by-step procedures to design and develop modern high-performance motor control algorithms will be introduced. And different typologies of electric motors used in traction applications will be considered, e.g., interior permanent magnet (IPM) synchronous machines, induction machines (IM), and the new generation of magnet-less motors like electric excited synchronous machines (EESMs). The course topic features several multidisciplinary aspects, including but not limited to i) dynamic modelling of ac motors and power electronics converters, ii) performance identification of electric motors, iii) design and implementation of high-efficient torque controllers for ac motors, and iv) digital control of electric motor drives for traction. The course has a significant applicative nature, and most of the presented concepts find direct application in the automotive industry. Actual traction motors used in automotive applications will be assumed as study cases in this course. And benchmark torque controllers to deal with these kinds of motors will be designed, developed, and implemented.

• Modelling of ac motors (6 h, 0.6 CFU): electromagnetic equations in dq coordinates, unified torque equation, magnetic saturation (apparent and differential inductances), flux and torque maps, losses models, space-state models, simulation models - Modelling of power electronics inverters (6 h, 0.6 CFU): 2-level structures, an overview of modulation techniques, losses models, voltage errors, simulation models - Identification of ac motors (8 h, 0.8 CFU): magnetic model identification procedures, direct- and inverse flux maps, inductance maps (apparent and differential), voltage-to-current models, scaling equations of loss maps - Manipulation of flux-, torque-, and losses- maps (12 h, 1.2 CFU): evaluation of MTPA-, MTPV-, and MTPS- profiles, efficiency mapping of ac motors, interpolation of n-dimensional maps - High-efficient and high-performance torque controllers for traction ac motors (12 h, 1.2 CFU): design and implementation of n-dimensional maps-based torque control algorithms - Discrete-time design and implementation of torque controllers (16 h, 1.6 CFU): proportional-integral regulators, filters, interpolation functions, stator flux observers, phase-locked loop, discretization techniques, simulation approaches

Modalita di esame: Prova orale obbligatoria; Elaborato progettuale individuale; Elaborato progettuale in gruppo;
Exam: Compulsory oral exam; Individual project; Group project;
... A list of projects will be provided to students. The students will be required to assemble into up to three-person teams based on commonality of interest. Each team will select a project from the project list. The mandatory oral exam consists in a final report that the entire team must present. The final report is a document (edited in Word or Latex) supported by simulation models. The presentation of the report must be performed in Powerpoint. The final score is calculated as the weighted arithmetic mean between the final report (60%) (evaluated for technical content, technical clarity and writing clarity) and the score for the presentation of the results during the oral exam (40%).

• Class notes (provided online) - IEEE articles (source: https://ieeexplore.ieee.org/Xplore/home.jsp) that can be accessed using Polito networks

• Knowledge of the main sensor types. - Knowledge of sensor connections to signal conditioning circuits. - Knowledge of operational amplifier circuits for sensors signal conditioning. - Knowledge of Analog-to-Digital Converters for sensor acquisition. - Knowledge of noise types in sensor systems. - Knowledge of serial communication interfaces. - Knowledge of microcontrollers. - Knowledge of memories for embedded systems. - Understanding of the physical principles of the main sensor types. - Understanding of how to model the transfer function of a sensor and how to calibrate the sensor. - Understanding of how to design a sensor to minimize noise. - Understanding of how to design power supply sources in sensor systems. - Ability to identify the optimal solution when designing a sensor. - Ability to design electronic circuits considering mixed signal constraints. - Ability to choose the best interface for the sensor connection to the external world.

The aim of this course is to give to the students all the instruments required to design a "smart sensor". The three main parts of such device, the sensor itself, the front-end analog electronic interface and the digital logic part are analyzed with great detail. These topics will be studied with a specific focus on practical aspects rather than theoretical ones. The goal is to teach to the students how to become a good system level designer, as requested by the profile of a Mechatronic Engineer, with a focus on mixed signal design of electronic circuits. A system level designer must have a wide knowledge of all the different parts of a system and he/she must be able to connect them together to create a working device. To reach this goal the course will combine a description of the different parts of a "smart sensor" focused toward practical implementations, exercises done during classes and a laboratory activity based on the development of a physical device. The laboratory activity will be structured in such a way that this result can be achieved both working from home or in a laboraory of the Politecnico.

• INTRODUCTION - Concepts of sensor, transducer and smart sensor (sensor module) - Sensor transfer functions - Sensor calibration - PART 1 - SENSOR ELEMENTS - Sensor characteristics - Dynamic models of sensor elements - Physical principles of sensing - Examples of sensors and their equivalent electric circuit - PART 2 - SIGNAL CONDITIONING (ANALOG ELECTRONICS) - Operational amplifier circuits for sensing - Sensor ratiometric and differentical connections - Bridges and bridge operational amplifiers - Excitation circuits - Multiplexers - Analog-to-Digital Converters (ADC) - Voltage-to-frequency (V/F), Pulse Width Modulator and Resistance-tp-frequency converters - Successive approximation (SAR) and sigma delta ADC - Resolution extension and ADC interfaces - Analog data transmission (2 and 4 point connections) - Noise - Types of noise - Shielding tecniques - Grounding techniques - Power supply - Batteries and supercapacitors - Principles of energy harvesting - Power Supply Noise Reduction and Filtering - Hardware design techniques - Resistor and Thermocouple Errors in High Accuracy Systems - Preventing RFI Rectification - Dealing With High Speed Logic - Isolation Techniques - Overvoltage Protection - Electrostatic Discharge (ESD) - Practical examples of sensor connections for specific types of sensors - PART 3 - DIGITAL ELECTRONICS - Serial busses - UART, I2C, SPI - Microcontrollers - Memories - EDRAM, FLASH, ROM, MRAM - Long range commnunication and sensors networks

Modalita di esame: Prova orale obbligatoria; Elaborato scritto prodotto in gruppo;
Exam: Compulsory oral exam; Group essay;
... The evaluation of the course is composed by two parts, the exam itself and the evaluation of the laboratory part. The oral exam consists of 3 to 5 questions. Three main parameters will be used to evaluate the students during the oral exam. - The knowledge of the different parts of a sensor (the sensor element, the analog front-end and the digital back-end) work; - The knowledge of how these parts are connected and how they interact; - The knowledge of what are the most important parameters of a sensor from a system level perspective. The evaluation of the laboratorie is based on a report that must be submitted by the students by the end of january. The report must contain the answers to the laboratory exercises and the description of the project and the results obtained. The report will be evaluated on the basis of different parameters: - The correctness of the laboratory exercises; - The structure of the report and the methodology used during the laboratories and the project; - The correct behavior of the designed sensor system; - The number of features included in the design. The final mark of the exam will keep into account both the theoretical part (2/3 of the mark) and the project (1/3 of the mark).

The main book used in this course is Jacob Fraden, "Handbook of Modern Sensors. Physics, Designs and Applications" - Fifth Edition, Springer Additional material will be uploaded on the Portale della Didattica by the teacher. All theoretical classes will be videorecorded.

Modalita di esame: Accertamento (esame senza voto);
Exam: Check;
... The graduation grade for the Master's degree will be calculated starting from the average of the exams, to which a maximum of 8 additional points can be added. Points available to the Committee (maximum 6): - maximum 4 points for the Thesis; - maximum 2 points for the Thesis presentation. Points for the academic career (maximum 2): - max 1 point based on the number of honors (0.5 points for every 5 credits of exams with honors) - max 1 point based on the duration of the studies: * 1 point for graduating within the November/December session of the 2nd year of enrollment; * 0.5 points for graduating within the March/April session of the 3rd year of enrollment; * 0 points for graduating from the July session of the 3rd year. [Students with a path that requires an additional semester: 3rd year March/April and July - 1 point 3rd year October and November/December - 0.5 points Students with initial enrollment in March without advances ("anticipo crediti"): 3rd year March/April and July - 1 point 3rd year October and November/December - 0.5 points] If the final grade is equal to or greater than 112.51, the student will be awarded the maximum grade of 110 with honors (110 cum laude).

Modalita di esame: Accertamento (esame senza voto);
Exam: Check;
... The graduation grade for the Master's degree will be calculated starting from the average of the exams, to which a maximum of 8 additional points can be added. Points available to the Committee (maximum 6): - maximum 4 points for the Thesis; - maximum 2 points for the Thesis presentation. Points for the academic career (maximum 2): - max 1 point based on the number of honors (0.5 points for every 5 credits of exams with honors) - max 1 point based on the duration of the studies: * 1 point for graduating within the November/December session of the 2nd year of enrollment; * 0.5 points for graduating within the March/April session of the 3rd year of enrollment; * 0 points for graduating from the July session of the 3rd year. [Students with a path that requires an additional semester: 3rd year March/April and July - 1 point 3rd year October and November/December - 0.5 points Students with initial enrollment in March without advances ("anticipo crediti"): 3rd year March/April and July - 1 point 3rd year October and November/December - 0.5 points] If the final grade is equal to or greater than 112.51, the student will be awarded the maximum grade of 110 with honors (110 cum laude).

Knowledge and understanding of the concepts of free-space propagation, scattering from stratified dielectrics, optical fields in anisotropic media and dielectric waveguides. Ability to analyze and design basic optical components. Knowledge of Electromagnetic Compatibility (EMC) issues related to signal integrity in high-speed electronics. Ability to assess cross-talk and signal distortions in the design of multi-signal transfer structures.

The course is taught in English. This teaching unit offers an in-depth coverage of coherent optics (photonics). This subject deals with the peculiar properties of laser light and is the basis for technological achievements such as fiber optics communication networks ('broad band access'), deformation or pollution optical sensors or devices used for biological investigations. In particular, both free-space and guided-wave propagation of optical fields are addressed. A number of fundamental optical components are described and the relevant design methods are presented. In addition, the student is exposed to signal-integrity issues of electromagnetic compatibility and quantitative methods are explained to assess such effects in multi-signal transfer structures.

Free space optics (geometrical optics, gaussian beams, mirrors and lenses, diffractive optical elements) (1CFU) Analysis and design of stratified dielectric structures (antireflection coatings, periodic structures and Bragg mirrors) (1CFU) Optical resonators, Fabry-Perot and Mach-Zehnder interferometers (1 CFU). Crystal optics and interaction with external fields (plane waves in anisotropic media, electrooptic, acoustooptic effects) (1 CFU) Guided wave optics (slab waveguides, optical fibers, mode coupling, directional couplers) (2 CFU) Electromagnetic compatibility for high speed electronics (signal integrity, cross talk in multiconductor transmission lines) (2 CFU)

Modalita di esame: Prova orale obbligatoria; Elaborato progettuale individuale;
Exam: Compulsory oral exam; Individual project;
... Oral exam (30-45 minutes) aiming at ascertaining if the expected learning outcomes indicated in section 2 have been reached. The students will run the programs developed in the home-works and will discuss the results on the basis of the theory presented in the course.

Lecture notes made available through the course website ("Portale della didattica"). Other texts for reference are: B. Saleh, M. Teich,"Elements of Photonics", Wiley 1991 K.Iizuka, "Engineering optics", Springer 1987

The aim of the course is to make available to the students the knowledge of the following topics: - Methods and algorithms for building mathematical models of real-world dynamical systems from sets of noise-corrupted experimental data focusing in particular on system identification techniques devoted to the quantification of the model uncertainty (set-membership identification). - Methods and algorithms for the data-based feedback control system design. - Software and Hardware tools and methods for rapid prototyping of control systems. Students are expected to develop the following skills: - Ability to derive the mathematical model of a dynamical system from sets of noise-corrupted data collected by performing suitable experiments on a real plant, though the application of modeling and identification tools/algorithms. - Ability to design a feedback control system able to guarantee fulfillment of the prescribed performance requirements on the real plant, through the application of data-based control methods and algorithms. - Ability to practically implement and test on a real plant the designed robust control system by exploiting suitable software/hardware tools for rapid prototyping.

The aim of this course is to develop students’ ability to apply advanced methods for robust system identification and data-based control of uncertain systems. Proposed system identification and data-based control techiques are applied to a set of carefully selected laboratory processes. Theoretical results in robust system identification and control are introduced/reviewed in the first part of the course. In the second part, students will be offered the opportunity to face a number of problems arising while applying robust system identification and data-based control theory and algorithms to experimental setups and data.

The course will focus on the following main subjects: - Fundamental principles of set-membership identification theory [about 20 hours] - Fundamental principles of data-based controller design [about 20 hours] - Fundamental notions about hardware/software tools for rapid prototyping of control systems (MATLAB real-time toolbox, LAbview, National Instrument PXI) [about 5 hours] - Application of set-membership identification and robust control methods and algorithms to real-world case studies and/or laboratory processes [about 15 hours].

Modalita di esame: Prova scritta in aula tramite PC con l'utilizzo della piattaforma di ateneo;
Exam: Computer-based written test in class using POLITO platform;
... The assessment will be performed through a written exam that will take place directly in the lab in order to check students’ ability to apply robust identification and data-based control techniques. Written examination in computer laboratory. The aim is to check the ability of the students to perform system modeling, identification and feedback control design based on a set of input-output data collected on the plant. A detailed written report describing the proposed solution is required. More precisely, the students is required to perform: (i) analysis of the provided input-output data (4 points) (ii) estimation of a plant model by means of system identfication techniques (12 points) (ii) design a data-based controller able to satify a set of performance specifications (12 points) (iii) write a summary report where (i) and (ii) are suitably documented (8 points) The exam will last 3 hours. During the exam, the students are only allowed to use MATLAB software and related user manuals.

The subject of the course is almost entirely based on the research activity of the teacher and collaborators. Therefore, reading materials is made by: - Selected chapters from the book "System Identification: Theory for the User (2nd Edition)" (author: Lennart Ljung; publisher: Prentice Hall, year: 1999)" - Lecture slides and notes provided by the teacher - Scientific publications by the teacher and co-workers - Laboratory practice handouts

By the end of this course, students will gain the following knowledge and skill: - Knowledge of the concept of a dynamical system together with its mathematical representations such as state equations and transfer functions. - Skill in computing the solution of the system state equations. - Knowledge of the stability properties of dynamical systems - Skill in studying the stability properties of dynamical systems. - Knowledge of the concept of feedback control of dynamical systems. - Knowledge of the main performance requirements of feedback systems. - Knowledge of the main analysis techniques in the time domain and in the frequency domain for the study of stability and performance of feedback control systems. - Skill in analyzing stability and performance of feedback control systems. - Knowledge of the design techniques of feedback controllers based on lead and lag functions. - Skill in designing feedback controllers for single input single output systems through lead, lag and PID functions. - Skill in evaluating the behaviour and performance of controlled systems through numerical simulation. - Knowledge of unstructured uncertainty models. - Skill in deriving weighting functions from performance specifications. - Knowledge of nominal and robust stability; nominal and robust performance. - Skill in the study of nominal and robust stability; nominal and robust performance. - Knowledge of the theory of robust control through H_infinity norm minimization. - Skill in the design of controllers through H_infinity norm minimization. - Knowledge of structured uncertainty modelling and the generalized plant. - Skill in representing structured uncertainty. - Skill in deriving the generalized plant. - Knowledge of robust stability and robust performance analysis through structured singular values. - Skill in the analysis of robust stability and robust performance through structured singular values. - Knowledge of Mu-bounds computation - Basic Knowledge of mu-synthesis. - Skill in the computation of mu-bounds.

The ability to deal with uncertainty is an essential part in feedback control. If the disturbances and the plant dynamics were completely known, there would be no need to use feedback in order to achieve a specified plant response. It is precisely due to uncertainties that feedback is necessary. In the classical controller design methods, plant uncertainties are taken into account by stability margins relating to amplitude and phase. The need to deal with uncertainties in feedback control in a methodical manner, has led to the development of a specific theory of robust control, that provides a structured treatment of the robustness of control systems against plant uncertainties, for both scalar and multivariable plants. The theory also comprises the solution of realistically formulated optimal control problems for uncertain plants. This course describes the main methods of modern robust control theory. Given the nominal plant model, the set of disturbances and the plant uncertainty set, the problem is to design the controller in such a way that some specified control performance is achieved. A minimum requirement is that the closed-loop system should be stable for all plants in the given uncertainty set. The feedback control system is then said to be robustly stable. For open-loop unstable plants the problem of finding a controller such that the closed loop is robustly stable is by itself a well-motivated control problem. This is called the robust stabilization problem. Summarizing, one of the most useful features of a properly designed feedback control system is robustness, i.e., the ability of the closed-loop system to perform satisfactorily despite large variations in the (open-loop) plant dynamics. The aim of this course is to provide methodologies and tools for the analysis and design of robust feedback control systems.

Course organization. Prerequisites. Course description. Exam rules. Major topics and course outline (1.5 hr). Control problem formulation and systems representations (3 hr). Internal stability and Bibo stability (1.5 hr). Frequency response representations (Bode plot and polar plot) and Nyquist stability criterion (6 hr). Feedback control systems steady-state response to polynomial references and disturbances, and to sinusoidal disturbances (6 hr). Transient requirements translation and the Nichols chart (1.5 hr). Frequency domain controllers design through loop-shaping (3 hr). Performance specifications and weighting functions (4.5 hr). Unstructured uncertainty models (4.5 hr). Nominal and robust stability; nominal and robust performance (4.5 hr). Robust control through H_infinity norm minimization (6 hr). Structured uncertainty modelling and the generalized plant (6 hr) Robust stability and robust performance analysis through structured singular values (7.5 hr). Mu-bounds computation and introduction to mu-synthesis (3 hr). Exam simulation: guidelines and solution (1.5 hr).

Modalita di esame: Elaborato scritto individuale;
Exam: Individual essay;
... Written examination in computer laboratory: based on computer aided analysis and design of a feedback control system, adequately documented through a written report. More precisely, it is required to (A) Understand and translate the design specifications into suitable weighting functions (12 - 16 points); (B) Design a controller that guarantees fulfillment of the assigned requirements, through H_\infinity optimization (6 - 8 points); (C) Report the obtained performance of the designed feedback control system (5 - 7 points); (D) Carry out mu-analysis to study robust stability and/or robust performance of the designed control system against structured uncertainty (5 - 7 points); (E) Provide orderly and clear presentation with legible handwriting (2 points). During the examination, lasting 4 hours, the student may consult the lecture slides provided by the teacher. Detailed instructions and rules are presented in due course.

Selected chapters from: (a) S. Skogestad and I. Postlethwaite, Multivariable feedback Control, John Wiley and Sons, 2010. (b) J. Doyle, B. Francis and A. Tannenbaum, Feedback Control Theory, 1992. (c) K. Zhou and J. C. Doyle, Essentials Of Robust Control, Prentice Hall, 1999. Lecture slides are available as well as laboratory practice handouts.

• Knowledge of the connectivity needs for a modern vehicle. - Knowledge of the in-vehicle protocols. - Knowledge of the V2x protocols. - Knowledge of tools for vehicle analysis. - Knowledge of localization technologies.

The course is taught in English. This is an eligible course for the Master Degree in Computer Engineering, focused on Embedded Systems, and it is eligible course for the Master Degree in Mechatronic Engineering. The course aims at presenting the most relevant network technologies for connected vehicles. Starting from the network architecture of a vehicle, the most relevant protocols will be described at the vehicle level, as well as the vehicle to infrastructure and the vehicle to vehicle levels. The course will also address the knowledge of simulation and analysis tools typically used in the industry such as Vector CANalyzer/CANoe. Moreover, localisation techniques will be presented.

Evolution of vehicle networks (0.5 credits) Electronic architecture of a modern vehicle (1.0 credit) In-vehicle protocols (1.5 credits) - LIN - CAN - Automotive Ethernet Network analysis tools (1.0 credit) The CAN dbc format - The CANalyzer/CANoe tools (1.0 credit) Introduction to localization protocols (GPS, GLONAS, Galileo) (0.5 credit) Introduction to V2x communication protocols and simulation tools (1.5 credit)

E' possibile sostenere l?esame in anticipo rispetto all?acquisizione della frequenza

The knowledge acquired during the course will regard the following subjects: properties of nonlinear systems; properties of feedback systems; modern control design methods for nonlinear systems; coordinate reference systems, rotations and translations; spacecraft/aircraft attitude kinematics and dynamics; spacecraft orbital dynamics; spacecraft/aircraft control design. The skills acquired during the course will be the following: understanding and analyzing the behavior of a dynamic system; developing advanced control algorithms for nonlinear systems; understanding and analyzing the behavior of a spacecraft/aircraft; developing advanced control algorithms for spacecraft/aircraft systems; developing simulation and control software in Matlab/Simulink. The student will learn how to use in a comprehensive way the acquired knowledge and skills in order to deal with new problems, without being limited to a small set of applications/case studies.

Control is a multi-disciplinary area, involving theoretical, numerical and hardware tools, finalized at modifying the behavior of real-world systems. Due to its nature, control is nowadays fundamental in most fields of science and technology, ranging from the "classical" aerospace, automotive, robotics and energy fields, to less "traditional" fields, e.g., related to biomedical, data analytics, communication and network applications. Starting from the observation that the majority of real-world dynamic systems are nonlinear, the first objective of the course is to provide the basic methodologies for analyzing the properties of a nonlinear system and for designing effective control algorithms, aimed at obtaining the desired behavior for the system variables of interest. The second objective of the course is to show how these methodologies can be applied to aerospace systems, allowing the accomplishment of the most challenging missions.

Nonlinear system analysis: basic notions on dynamic systems; state equations; basic stability concepts; Lyapunov stability. Control design for nonlinear systems. Overview on different approaches: linearization and gain scheduling; feedback linearization; embedded model control; sliding-mode control; nonlinear model predictive control. Observer design for nonlinear systems: extended Kalman filter. Aerospace topics: coordinate reference systems; rotations and translations; rigid body attitude kinematics and dynamics; orbital dynamics. Aerospace applications/case studies will be about spacecraft orbit/trajectory control; spacecraft attitude control; aircraft flight control.

Modalita di esame: Test informatizzato in laboratorio;
Exam: Computer lab-based test;
... Written examination (carried out in lab with the help of the PC and the Matlab/Simulink software) with multiple choice questions and design problems. The number of questions will range between 7 and 11, depending on the average difficulty. A (small) negative score will be assigned to wrong answers. The duration of the exam will be 2.30 hours. The following material will be available during the exam: lecture slides (without students' notes), Matlab libraries. No other material will be allowed (in particular, no solved exercises). No oral examinations will be held.

[1] C. Novara, Nonlinear Control and Aerospace Applications: lecture notes. Politecnico di Torino, 2017. [2] J-J. E. Slotine and W. Li, Applied Nonlinear Control, Prentice Hall, 1991. [3] S. Sastry, Nonlinear Systems: Analysis, Stability, and Control, Springer, 1999. [4] M. H. Kaplan, Modern Spacecraft Dynamics and Control, I. John Wiley and Sons, 1976. [5] B. Wie, Space Vehicle Dynamics and Control. Aiaa, 1998. [6] F. Markley and J. Crassidis, Fundamentals of Spacecraft Attitude Determination and Control. Cambridge University Press, 2014. [7] D. G. Hull, Fundamentals of Airplane Flight Mechanics, Springer, 2007. [8] A. Tewari, Atmospheric and Space Flight Dynamics: Modeling and Simulation with Matlab and Simulink, Birkhauser, 2007. [9] E. Canuto, C. Novara, L. Massotti, C. Perez Montenegro and D. Carlucci, Spacecraft dynamics and control. The embedded model control approach, Butterworth-Heinemann (Elsevier), 2018.

• Concept of embedded system. - Understanding of the development tools for embedded systems: cross-compiler, emulator, debug tools, profiling tools, virtualization tools. - Concept of real-time embedded systems. - Understanding of examples of operating systems for embedded systems. - Skill for the development of modules for the communication with ad-hoc hardware components.

The course is taught in English. This course is part of the Master Degree in Computer Engineering, focused on Embedded Systems, and it is eligible course for the Master Degree in Mechatronic Engineering. The course aims at deepening the concepts acquired during the Operating Systems course, describing the peculiarities of embedded systems, of the dedicated development tools, and outlining the peculiarities of some operating systems for embedded systems that are particularly relevant (process scheduler, OSEK/VDX, AUTOSAR, Linux). The course will also deepen the concepts of real-time embedded systems, and the issues of communications with ad-hoc hardware components.

Introduction to Embedded Systems (0.5 CFU) - Classification with respect to desktop systems. - Memory technology device. Development tools (0.5 CFU) - Cross-compiler. - Emulator. - Debugger. - Profiler. - Virtual platforms. Operating Systems for Embedded Systems (4 CFU) - Real time and process scheduling. - Analysis of the architecture and services offered by operating systems for embedded: process scheduler, VxWorks, OSEK/VDX, AUTOSAR, Linux. - Anatomy of an operating system for embedded systems. Programming of an embedded OSEK/VDX system (1 CFU): - I/O management, and device drivers. - Use of an evaluation board. - Building a OSEK/VDX operating system for embedded systems: how to use development tools. - Design and development of device drivers for custom devices.

E' possibile sostenere l?esame in anticipo rispetto all?acquisizione della frequenza

Notes provided by the teacher. Additional reading: A. Berger Embedded Systems Design: An Introduction to Processes, Tools and Techniques CMP Books, 2001, ISBN-10: 1-57-820073-3 Silbershatz A., Galvin. P., Gagne G. Operating Systems 8th Edition, Wiley, 2009, ISBN: 978-0-470-12872-5

• To identify problems that can be tackled through simulation or optimization in the context of different areas of application. - To know the basics of discrete event simulation, with the goal of simulating a production system. - To apply to simulation experiments the main mathematical and statistical tools, and quantitative methods in general, in order to derive from raw data meaningful figures of merit for the simulated systems. - To build simulation models, design and carry out a whole simulation project. - To know how to model a combinatorial optimization problem through a linear programming model. - To understand the basic optimization concepts and the most adequate optimization techniques (exact, heuristic) for different problem types. - To learn to use the Matlab simEvents package to carry out the simulation of a discrete-event system. - To learn to use a mathematical programming solver (Xpress Solver, Matlab Optimization Toolbox, etc.) for solving combinatorial optimization problems.

Optimization and Simulation are powerful software tools for automation that can be applied at different abstraction levels: from supply chain management, logistics and long-term production planning to the scheduling and control of a single station or robot. Objective of this module is to provide methods for modeling, simulating, and optimizing systems of different types, mostly for automation applications. The first part of the module will focus on the development of simulation models, mainly for stochastic dynamical discrete-event systems, providing methods for reaching a better understanding of how systems operate and respond to input changes. The second part will focus on optimization, introducing methods for formalizing optimization problem (Linear Programming Models) and methods for solving them (both exact and approximate).

• Introduction: Automation, Simulation, Optimization. - Basics of Simulation: modelling and different types of simulation techniques (Continuous, Discrete, Discrete Event). - Discrete Event Simulation. - Techniques for the analysis of the output data of a discrete event simulator. - The fundamentals of the Matlab SimEvents package. - Laboratory exercises: simulate discrete event systems with SimEvents. - Combinatorial Optimization and Linear Programming models. - Exact algorithms (Branch and Bound) and heuristic algorithms (Greedy, Local Search, Metaheuristics, Tabu Search, Genetic Algorithms, Matheuristics). - Real world optimization applications. - Laboratory exercises: model and solve linear programming models using different tools (Xpress Solver, Matlab Optimization Toolbox).

Modalita di esame: Prova scritta (in aula); Elaborato progettuale in gruppo;
Exam: Written test; Group project;
... The exam is written, and based on a set of questions and exercises, with the objective of verifying the above mentioned competences (Expected Learning Outcomes). The exam duration is 1,5 hours. It is not possible to consult texts, lecture notes and forms. The exam gives a maximum of 27 points (minimum 18 for passing). A bonus to the final exam mark is given by a mandatory laboratory group project, giving a maximum of 6 points. The final evaluation is the sum of the points obtained with the exam and the laboratory project (with 31 being still 30, and 32 being 30L). Laboratory marks are kept valid for duration of the academic year, in the case a student did not pass the written exam.

Slides and other learning material provided by the instructor. A list of relevant books on the course topics: D. J. Luenberger, "Linear and Nonlinear Programming", Springer, 3rd ed., 2008. G.L. Nemhauser, L.A. Wolsey, "Integer and Combinatorial Optimization", Wiley, 1999. G. Ausiello, A. Marchetti-Spaccamela, P. Crescenzi, G. Gambosi,M. Protasi, V. Kann, "Complexity and Approximation: Combinatorial Optimization Problems and Their Approximability Properties", Springer, 2003, 2nd edition. R. Tadei, F. Della Croce, "Elementi di Ricerca Operativa", Editrice Esculapio. R. Tadei, F. Della Croce, A. Grosso, "Fondamenti di Ottimizzazione", Editrice Esculapio. M. Ghirardi, A. Grosso, G. Perboli, "Esercizi di Ricerca Operativa", Editrice Esculapio. A.M. Law, "Simulation Modeling and Analysis", McGraw-Hill, 2015. Cassandras, Lafortune, "Introduction to Discrete Event Systems", Springer. Simulation Modeling Handbook: A Practical Approach, Christopher A. Chung (Ed.) CRC Press: New York, NY., 2004 Discrete-Event System Simulation, Jerry Banks, at al., Prentice Hall: Upper Saddle River, NJ., 2005 Principles of Modeling and Simulation: A Multidisciplinary Approach, John A. Sokolowski (Editor), Catherine M. Banks (Editor) John Wiley & Sons, 2009

The student will acquire knowledge of the basic tools used in machine learning, and an introductory insight on the functioning of the optimization algorithms that form the inner computational “engine� of these tools. The student will gain some experience in visualizing and analyzing labeled and unlabeled high-dimensional data sets and in extracting useful information from them. Complementing a student’s background in statistics, optimization or data mining, this course will help forming the skills of a Junior Data Scientist.

Machine Learning (ML) encompasses a variety of methodologies and computational algorithms, mainly grounded in Bayesian statistics, for extracting information, clustering, detecting patterns, making decisions and predictions or, more generally, understanding phenomena from available data. Classical learning models created in the 1970s, such as Neural Networks, as well as later techniques such as Support Vector Machines (SVM), are witnessing a new wave of resurgence in both theory and applications in the present era of Big Data, where the deluge of unstructured information calls for automated and highly efficient methods of data analysis. Contemporary Machine Learning, in turn, constitutes an essential part of Data Science, an interdisciplinary field for which industry has a global excess demand of experts. In this course, we present the main tools for supervised learning (regression, regularization, classification) and unsupervised learning (clustering, dimensionality reduction), with a focus on the structure and features of the optimization algorithms that are needed to actually solve numerically the learning problems of interest. The course is structured into lectures in class, in which the context and methodologies are explained, and computer lab sessions, in which the students apply the methodologies to real-world data sets and problems coming from various fields, such as finance, business analytics, news, biology, medical diagnosis, etc.

Introduction to Machine Learning. Supervised and unsupervised learning. Parametric and nonparametric models. Classical examples in pattern analysis (e.g., hand-writing recognition). A brief historical perspective. Review of probability theory and statistics. Marginal and conditional distributions. Bayes theorem. Prior, likelihood, posterior. Bayesian inference. Regression problems. Over-fitting. Bias-variance tradeoff. Regularized regression. Linear regression with sparsity-inducing penalties. Ridge regression. The Lasso. The Elastic-Net. Applications (e.g., in image analysis and in computational finance). Logistic regression. Sparse logistic regression and applications (e.g., to text categorization). Algorithms for large-scale regularized regression: First-order methods. Proximal methods. The Fast Iterative Shrinkage-Thresholding Algorithm (FISTA). Coordinate descent and block-coordinate descent methods. Classifiers. Neural Networks. Training and the back-propagation algorithm. Maximum margin classifiers. Dual representation. Kernel methods and the Support Vector Machine (SVM). Clustering. K-means clustering. Gaussian mixtures and the Expectation-Minimization (EM) algorithm. Singular value decomposition and the Principal Component Analysis (PCA). Interpretability and the Sparse-PCA. Fast algorithms for Sparse-PCA.

Modalita di esame: Prova scritta (in aula);
Exam: Written test;
... The final exam consists in a written test, which will contain a mixture of methodological questions and numerical exercises (to be executed with pen and paper; use of a calculator is allowed). Depending on the numerosity of the course and exam participants, the written examination may take either the form of an open question test, or the form of a multiple-choice questionar.

Course slides, handouts, and lab practice sheets will be made available to the students via the PoliTo Web portal. Useful reference textbooks are the following ones: C.M. Bishop, Pattern Recognition and Machine Learning, Springer, 2006. J. Friedman, T. Hastie and R. Tibshirani, The Elements of Statistical Learning, Springer, 2009. G.C. Calafiore and L. El Ghaoui, Optimization Models, Cambridge Univ. Press, 2014.

At the end of this course the following learning outcomes will be achieved, regarding Orbital Robotics (OR) and Distributed Space Systems (DSS). The student will: -know and comprehend, at a system engineering level, the fundamental elements pertaining to their Mechanics, and Guidance, Navigation & Control aspects; -know and comprehend the classification of several possible architectures; -know and comprehend the mathematical description of kinematics (for the translational, rotational and internal configuration), differential kinematics, and kinetics; -know and comprehend how to model the most common external actions (due to environment and actuators) as well as internal actions (due to internal dynamics and actuators); -know and comprehend selected analytical methods and techniques, based on Newtonian and analytical mechanics, for formulating mathematical models of the dynamics; -be able to apply the acquired methods for writing the equations of motion of specific instances of spacecraft-manipulator systems by leveraging symbolic computation; -know and comprehend selected method for the synthesis of autonomous Guidance, Navigation & Control logics to accomplish selected desire maneuvers; -be able to code prototype software to perform numerical simulation of their natural and forced motion; -be able to validate that software, i.e., to check whether it fulfills its intended purpose; -be able to use that software to preliminarily verify, via numerical simulation experiments, key performances (e.g., the satisfaction of selected requirements regarding guidance and control) by specific instances of OR or DSS systems, during sample maneuver scenarios; -be familiar, at a system engineering level, with the current technology pertaining to Orbital Robotics and Distributed Space Systems; -gain a general engineering intuition in terms of orders of magnitude of quantities involved, system capabilities, limitations, and possible innovative systems and applications.

Orbital robotics systems, that are covered in the first part of this course, are artificial satellite systems that can physically interact with another orbiting object by using actively controlled on board mechanisms that enable grasping, manipulation and mobility: a paradigmatic example is an artificial satellite equipped with an onboard robotic manipulator. Applications of orbital robotics systems include On-orbit Servicing, Assembly and Manufacturing, and space debris removal. Distributed Space Systems, that are covered in the second part of this course, are artificial satellite systems that occupy a region considerably larger than the one pertaining to a single traditional artificial satellite. Examples of Distributed Space Systems are Formation Flying Satellites, Tethered Satellites and Very Large Satellite Systems. Applications of Distributed Space Systems include: high-resolution Earth or astronomic observation, and collection and retransmission of solar power. This course will transfer knowledge of analytical methods and formulation practices for developing mathematical models of motion for classes of Orbital Robotics and Distributed Space Systems, under the effect of both environmental actions as well as actions by on-board actuators, such as thrusters and momentum exchange devices. Furthermore, the course will build comprehension of how these mathematical models of motion are used for the synthesis of autonomous guidance, navigation and control algorithms that enable the execution of translational, rotational and reconfiguration maneuvers to achieve specific mission capabilities. Finally, the course will develop comprehension on how software libraries are coded and used to perform numerical simulations to study the physical phenomenon of motion in specific instances of Orbital Robotics and Distributed Space Systems, and to assess the performance of selected guidance, navigation and control logics during specific maneuver scenarios. Examples will be considered of space systems encompassing current and emerging space technology solutions, and of maneuver scenarios pertaining to real space missions, either previously flown or planned.

20 hours each for Orbital Robotics Systems and Distributed Space Systems regarding: mathematical modeling of motion, synthesis of guidance, navigation and control methods, system engineering and technology 10 hour each for Orbital Robotics Systems and Distributed Space Systems regarding: coding of prototype simulation software, execution of numerical experiments of validation and verification

Modalita di esame: Prova scritta (in aula); Elaborato progettuale individuale;
Exam: Written test; Individual project;
... The exam is aimed at ascertaining knowledge of the topics listed in the official teaching program and the ability to apply the theory and related calculation methods to solving exercises. The evaluations are expressed out of thirty and the exam is passed if the score reported is at least 18/30.

The course reading materials will consist primarily of class notes developed by Prof. Romano, and distributed during the course. Other reference materials (as textbook excerpts and specialized papers) will be indicated during the course.

After this course, a student will have a detailed knowledge of the basic DC-DC power conversion topologies, in particular buck, boost, buck boost, their working modes. He or she will know some methods for deriving a linear small signal model of switching converters. Beside the non-isolated topologies, a student will learn the detailed behavior of the most common derived converters using an isolation transformers, including their small signal models. By the end of this course, a student will be able to design both the power and control section of an isolated converter, choosing the best among various different topologies and control methods, and estimating the component stress and total circuit efficiency.

This course provides the student with an in depth knowledge about the themes of systems and circuits used for powering electronic circuits and small electromechanical actuators. Students will study mainly DC-DC converters, analyzing energy efficiency, control, isolation and electromagnetic noise generation which shouldn’t inject disturbances in the mains. This class has a strong design flavor: most important methods of analysis and design both for power and control stages will be presented.

Basic DC/DC converters: Buck, Boost and Buck-Boost. Steady state (cyclostationary) characteristics in continuous and discontinuous conduction mode, design criteria and stress evaluations (1.8 CFU) Dynamic behavior. Averaged models, state space average, switch average, circuit average. Linearization. Power factor correctors (1.6 CFU) Design of closed loop control, both voltage mode and current mode (0.8 CFU) Derived configurations: Analysis and design of Buck-derived circuits (Forward, push-pull, half bridge, full bridge). Analysis and design of a flyback. Design of control section (1.8 CFU)

Erickson – Maksimovic, Fundamentals of Power Electronics, Springer; 3rd edition (August 2020) Kassakian, Schlecht, Verghese, “Principles of Power Electronics�, Addison Wesley

• Ability to use mathematical tools to model and control space systems, including complex ones, with an understanding of flight determination and control problems. • Understand the issues related to orbital flight and satellite dynamics, focusing on rendezvous and docking maneuvers. • Acquire the ability to simulate rendezvous and docking maneuvers using design tools commonly employed in industrial environments (MATLAB, Simulink).

The main objective of the course is to study and design the dynamics and control of attitude and trajectory of autonomous and semi-autonomous space vehicles. The problems related to modeling and control will be addressed, introducing the mathematical tools required to design and solve the problems under analysis. The study of orbital maneuvers, proximity flight and rendezvous and docking maneuvers is part of the course objectives.

• Historical background, examples, and classifications of reference vehicles and missions (definition of the technical context). • Introduction to proximity maneuvers (orbital maneuvers, proximity flight, rendezvous and docking): reference frames and orbital parameters. • Review of rigid body dynamics: attitude representation (Euler angles and quaternions). Euler’s equation and its generalized form. Hill’s equations for circular orbits in inertial and local reference frames. Torque free motion of spinning bodies in the absence of external torques. • The space environment and its effects on translational and attitude dynamics. Disturbance forces and torques. The problem of attitude motion stability and control (active and passive stabilization, dual-spin satellites, dissipation effects, orbital stabilization). • Review of basic control concepts (definition of open- and closed-loop systems, time-response analysis). Overview of control algorithms and their implementation (hardware and software), ranging from classical (robust) control laws to variable-structure control laws (sliding mode control). • Orbital proximity operations and docking maneuvers (orbital maneuvers, proximity flight, rendezvous and docking). Analysis of maneuver types and guidance algorithms. Types of actuators. Analysis of the components of an orbital simulator. • Introduction to the problem of position and attitude determination: sensors and algorithms. Experimental systems and industrial software and hardware-in-the-loop simulators. Limits of validity of the rigid-body assumption for space vehicles: “flexible� satellites and robotic arms. In addition to the theoretical lectures, the student will also carry out practical exercises on the following topics: • Exercise 0: Rigid body dynamics with quaternions. Introduction to Hill’s Equations • Exercise 1: Attitude dynamics (single-axis) – Feedback control with pole placement. PWPF modulation for thruster-type actuators. • Exercise 2: Attitude dynamics (3-axes) – Implementation of an LQR controller with Reaction Wheel (RW) actuators. • Exercise 3: Position dynamics (Hill’s Equations) – Free drift + Hohmann maneuver in open-loop. Implementation of a Sliding Mode Controller (SMC) to track the trajectory. Moreover, an orbital flight simulator for a 6 degree of freedom spacecraft will be designed and implemented.

Modalita di esame: Prova scritta (in aula); Prova orale obbligatoria; Elaborato scritto individuale; Elaborato progettuale in gruppo;
Exam: Written test; Compulsory oral exam; Individual essay; Group project;
... The exam includes the following parts: • Preparation of a report related to the practical exercises carried out during the course (3 points) and to the design of an orbital simulator for rendezvous and docking maneuvers (7 points). This document must be submitted in its COMPLETE form at least two days before the exam session scheduled for the written test. The evaluation of the produced work will be individual (maximum 10/30). The software may be developed in groups of 3–4 students, but the report must be prepared individually. • The evaluation of the theoretical part of the course is divided into two parts: (1) a written test consisting of 6 questions: 2 descriptive questions and 4 theoretical questions (maximum 16/30); (2) an oral examination in which both the course topics and the written-test questions will be discussed. The discussion of the practical exercises also contributes to the final evaluation (maximum 6/30). The oral examination is mandatory even if the sum of the written test and the report evaluation exceeds 18/30. To be admitted to the oral examination, the student must obtain at least 10/16 in the written test.

De Ruiter et.al., "Spacecraft Dynamics and Control: An Introduction", Wiley, 2013. Fehse, "Automated Rendezvous and Docking of Spacecraft", Cambridge Aerospace Series, 2003. Markeley et.al., "Fundamentals of Spacecraft Attitude Determination and Control", Springer, 2014.