Control Engineering

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Curriculum(s) for 2024 - Control Engineering (29933)

Single curriculum

Lesson [SSD] [Language]	Year	Semester	CFU
10612312 \| NONLINEAR SYSTEMS AND CONTROL [ING-INF/04] [ITA]	1st	1st	12
Educational objectives Obiettivi generali Il corso presenta i metodi di base per studiare le proprietà geometriche dei sistemi di controllo a partire da modelli non lineari. I modelli ai quali si farà riferimento sono quelli caratterizzati da una struttura differenziale affine rispetto al controllo; modelli adatti a rappresentare una larga varietà di processi di interesse nelle applicazioni dell'ingegneria. Obiettivi specifici Conoscenza e comprensione: Lo studente sarà in grado di comprendere i concetti fondamentali della teoria del controllo non lineare e geometrico. Conoscerà principali strumenti matematici utilizzati nell'analisi e nella progettazione dei sistemi di controllo non lineare. Saprà comprendere le proprietà geometriche dei sistemi dinamici non lineari e il loro impatto sulla progettazione del controllo. Applicare conoscenza e comprensione: Applicare i concetti presentati per risolvere diversi problemi di controllo (e.g., regolazione, asservimento di traiettorie, disaccoppiamento dei disturbi, non interazione) per sistemi dinamici, tenendo conto delle specifiche prestazioni richieste e delle limitazioni del sistema. Utilizzare metodi avanzati di analisi dei sistemi non lineari per prevedere e comprendere il comportamento del sistema in una vasta gamma di condizioni operative. Condurre esperimenti virtuali e simulazioni numeriche per valutare l'efficacia delle strategie di controllo proposte e confrontare le prestazioni con i sistemi lineari. Capacità critiche e di giudizio: Lo studente sarà in grado di valutare criticamente le limitazioni delle approssimazioni lineari nell'analisi e nel controllo dei sistemi non lineari e identificare situazioni in cui tali approssimazioni possono portare a risultati inaccurati o inadeguati. Inoltre, saprà analizzare in modo critico i risultati delle simulazioni e dei test sperimentali per valutare l'efficacia delle strategie di controllo proposte e identificare possibili miglioramenti. Infine, potrà valutare l'applicabilità delle soluzioni di controllo proposte in contesti ingegneristici reali, considerando vincoli di implementazione, costi e risorse disponibili. Sarà, infine, in grado di leggere in maniera critica articoli scientifici. Capacità comunicative: Lo studente sarà in grado di comunicare in modo chiaro e efficace i concetti teorici e le metodologie di progettazione relative ai sistemi non lineari e al controllo, sia verbalmente che per iscritto. Saprà presentare in modo chiaro e convincente i risultati delle analisi e delle simulazioni attraverso relazioni tecniche, presentazioni orali e documenti tecnici. Potrà collaborare in modo efficace con altri studenti e professionisti nel contesto del progetto e dell'implementazione di soluzioni di controllo per sistemi non lineari, comunicando in modo chiaro e conciso le proprie idee e opinioni. Capacità di apprendimento: Le modalità di svolgimento del corso mirano a sviluppare la capacità di saper comprendere metodi diversi, eventualmente elaborarne di individuali, nella soluzione dei problemi di analisi e controllo allo studio.
Nonlinear control systems analysis [ING-INF/04] [ITA]	1st	1st	6
Educational objectives Obiettivi generali Il corso presenta i metodi di base per studiare le proprietà geometriche dei sistemi di controllo a partire da modelli non lineari. I modelli ai quali si farà riferimento sono quelli caratterizzati da una struttura differenziale affine rispetto al controllo; modelli adatti a rappresentare una larga varietà di processi di interesse nelle applicazioni dell'ingegneria. Obiettivi specifici Conoscenza e comprensione: Lo studente sarà in grado di comprendere i concetti fondamentali della teoria del controllo non lineare e geometrico. Conoscerà principali strumenti matematici utilizzati nell'analisi e nella progettazione dei sistemi di controllo non lineare. Saprà comprendere le proprietà geometriche dei sistemi dinamici non lineari e il loro impatto sulla progettazione del controllo. Applicare conoscenza e comprensione: Applicare i concetti presentati per risolvere diversi problemi di controllo (e.g., regolazione, asservimento di traiettorie, disaccoppiamento dei disturbi, non interazione) per sistemi dinamici, tenendo conto delle specifiche prestazioni richieste e delle limitazioni del sistema. Utilizzare metodi avanzati di analisi dei sistemi non lineari per prevedere e comprendere il comportamento del sistema in una vasta gamma di condizioni operative. Condurre esperimenti virtuali e simulazioni numeriche per valutare l'efficacia delle strategie di controllo proposte e confrontare le prestazioni con i sistemi lineari. Capacità critiche e di giudizio: Lo studente sarà in grado di valutare criticamente le limitazioni delle approssimazioni lineari nell'analisi e nel controllo dei sistemi non lineari e identificare situazioni in cui tali approssimazioni possono portare a risultati inaccurati o inadeguati. Inoltre, saprà analizzare in modo critico i risultati delle simulazioni e dei test sperimentali per valutare l'efficacia delle strategie di controllo proposte e identificare possibili miglioramenti. Infine, potrà valutare l'applicabilità delle soluzioni di controllo proposte in contesti ingegneristici reali, considerando vincoli di implementazione, costi e risorse disponibili. Sarà, infine, in grado di leggere in maniera critica articoli scientifici. Capacità comunicative: Lo studente sarà in grado di comunicare in modo chiaro e efficace i concetti teorici e le metodologie di progettazione relative ai sistemi non lineari e al controllo, sia verbalmente che per iscritto. Saprà presentare in modo chiaro e convincente i risultati delle analisi e delle simulazioni attraverso relazioni tecniche, presentazioni orali e documenti tecnici. Potrà collaborare in modo efficace con altri studenti e professionisti nel contesto del progetto e dell'implementazione di soluzioni di controllo per sistemi non lineari, comunicando in modo chiaro e conciso le proprie idee e opinioni. Capacità di apprendimento: Le modalità di svolgimento del corso mirano a sviluppare la capacità di saper comprendere metodi diversi, eventualmente elaborarne di individuali, nella soluzione dei problemi di analisi e controllo allo studio.
Nonlinear control systems design [ING-INF/04] [ITA]	1st	1st	6
Educational objectives General objectives The course presents the basic methodologies for studying the properties of processes described by a nonlinear model and designing ad-hoc control laws for enforcing a desired behavior. The models at hand are described by a system of differential equations, assumed affine in the control. These models are successfully used to describe a variety of processes of interest in the engineering domain. Specific objectives Knowledge and understanding: The student will be able to understand the fundamental concepts for the analysis of nonlinear systems and control, including stability concepts and those that make use of differential geometry. The student will be able to distinguish the dynamic behaviors of linear and nonlinear systems. They will know the main control techniques for nonlinear systems to solve various problems such as stabilization, regulation, and tracking. Apply knowledge and understanding: Apply the acquired knowledge to design and implement effective controllers for nonlinear systems, taking into account the specific required performance and system limitations. Utilize advanced methods of nonlinear systems analysis to predict and understand the system's behavior under a wide range of operating conditions. Conduct virtual experiments and numerical simulations to assess the effectiveness of proposed control strategies and compare performance with linear systems. Critical and judgment skills: The student will be able to critically evaluate the limitations of linear approximations in the analysis and control of nonlinear systems and identify situations where such approximations may lead to inaccurate or inadequate results. Additionally, they will be able to critically analyze the results of simulations and experimental tests to assess the effectiveness of proposed control strategies and identify potential improvements. Finally, they will be able to evaluate the applicability of proposed control solutions in real engineering contexts, considering implementation constraints, costs, and available resources. They will also be able to critically read scientific articles. Communication skills: The student will be able to communicate theoretical concepts and design methodologies related to nonlinear systems and control clearly and effectively, both verbally and in writing. They will know how to present analysis and simulation results convincingly through technical reports, oral presentations, and technical documents. They will be able to collaborate effectively with other students and professionals in the context of designing and implementing control solutions for nonlinear systems, communicating their ideas and opinions clearly and concisely. Learning ability: The course methods aim to develop the ability to understand different methods, possibly devising individual ones, in solving problems related to analysis and control under study.
10596148 \| Filtering and Optimal Control [ING-INF/04] [ENG]	1st	1st	12
Educational objectives General goals The course illustrates the methodologies of optimal control and filtering. The student will be able to formulate, analyze, and search for solutions of estimation, filtering and optimization problems of different nature. Specific outcomes - Knowledge and understanding The Students will lean the methods of filtering and optimal control theory and the main applications in different fields. - Capability to apply knowledge and understanding The Students will be able to propose the optimal control and estimation strategies depending on the specific problem at hand. - Critical and judgment skill The Students will be able to analyze control problems, proposing and implementing optimal control strategies. - Communication skills The course activities will allow the Students to increase their communication capabilities in scientific fields. - Learning skills One of the aim of the course is to increase the critical skills of the Students; they will be able to tackle a control problem, starting from the literature analysis, to the optimal control and filtering aspects, up to the implementation.
Module I [ING-INF/04] [ENG]	1st	1st	6
Educational objectives General goals (Mod. I) The course illustrates the methodologies of optimal control. The student will be able to formulate, analyze, and search for solutions of optimization problems of different nature by an appropriate use of optimality conditions, with emphasis on optimal control problems. Specific outcomes - Knowledge and understanding The Students will lean the methods of optimal control theory and the main applications in different fields. - Capability to apply knowledge and understanding The Students will be able to propose the optimal control strategies depending on the specific problem at hand. - Critical and judgment skill The Students will be able to analyze control problems, proposing and implementing optimal control strategies. - Communication skills The course activities will allow the Students to increase their communication capabilities in scientific fields. - Learning skills One of the aim of the course is to increase the critical skills of the Students; they will be able to tackle a control problem, starting from the literature analysis, to the optimal control aspects, up to the implementation.
Module II [ING-INF/04] [ENG]	1st	1st	6
Educational objectives General goals (Mod. I) The course illustrates the methodologies of optimal control. The student will be able to formulate, analyze, and search for solutions of optimization problems of different nature by an appropriate use of optimality conditions, with emphasis on optimal control problems. Specific outcomes - Knowledge and understanding The Students will lean the methods of optimal control theory and the main applications in different fields. - Capability to apply knowledge and understanding The Students will be able to propose the optimal control strategies depending on the specific problem at hand. - Critical and judgment skill The Students will be able to analyze control problems, proposing and implementing optimal control strategies. - Communication skills The course activities will allow the Students to increase their communication capabilities in scientific fields. - Learning skills One of the aim of the course is to increase the critical skills of the Students; they will be able to tackle a control problem, starting from the literature analysis, to the optimal control aspects, up to the implementation.
10612312 \| NONLINEAR SYSTEMS AND CONTROL [ING-INF/04] [ITA]	1st	2nd	12
Educational objectives General objectives The course presents the basic methodologies for studying the properties of processes described by a nonlinear model and designing ad-hoc control laws for enforcing a desired behavior. The models at hand are described by a system of differential equations, assumed affine in the control. These models are successfully used to describe a variety of processes of interest in the engineering domain. Specific objectives Knowledge and understanding: The student will be able to understand the fundamental concepts for the analysis of nonlinear systems and control, including stability concepts and those that make use of differential geometry. The student will be able to distinguish the dynamic behaviors of linear and nonlinear systems. They will know the main control techniques for nonlinear systems to solve various problems such as stabilization, regulation, and tracking. Apply knowledge and understanding: Apply the acquired knowledge to design and implement effective controllers for nonlinear systems, taking into account the specific required performance and system limitations. Utilize advanced methods of nonlinear systems analysis to predict and understand the system's behavior under a wide range of operating conditions. Conduct virtual experiments and numerical simulations to assess the effectiveness of proposed control strategies and compare performance with linear systems. Critical and judgment skills: The student will be able to critically evaluate the limitations of linear approximations in the analysis and control of nonlinear systems and identify situations where such approximations may lead to inaccurate or inadequate results. Additionally, they will be able to critically analyze the results of simulations and experimental tests to assess the effectiveness of proposed control strategies and identify potential improvements. Finally, they will be able to evaluate the applicability of proposed control solutions in real engineering contexts, considering implementation constraints, costs, and available resources. They will also be able to critically read scientific articles. Communication skills: The student will be able to communicate theoretical concepts and design methodologies related to nonlinear systems and control clearly and effectively, both verbally and in writing. They will know how to present analysis and simulation results convincingly through technical reports, oral presentations, and technical documents. They will be able to collaborate effectively with other students and professionals in the context of designing and implementing control solutions for nonlinear systems, communicating their ideas and opinions clearly and concisely. Learning ability: The course methods aim to develop the ability to understand different methods, possibly devising individual ones, in solving problems related to analysis and control under study.
Nonlinear control systems analysis [ING-INF/04] [ITA]	1st	2nd	6
Educational objectives Obiettivi generali Il corso presenta i metodi di base per studiare le proprietà geometriche dei sistemi di controllo a partire da modelli non lineari. I modelli ai quali si farà riferimento sono quelli caratterizzati da una struttura differenziale affine rispetto al controllo; modelli adatti a rappresentare una larga varietà di processi di interesse nelle applicazioni dell'ingegneria. Obiettivi specifici Conoscenza e comprensione: Lo studente sarà in grado di comprendere i concetti fondamentali della teoria del controllo non lineare e geometrico. Conoscerà principali strumenti matematici utilizzati nell'analisi e nella progettazione dei sistemi di controllo non lineare. Saprà comprendere le proprietà geometriche dei sistemi dinamici non lineari e il loro impatto sulla progettazione del controllo. Applicare conoscenza e comprensione: Applicare i concetti presentati per risolvere diversi problemi di controllo (e.g., regolazione, asservimento di traiettorie, disaccoppiamento dei disturbi, non interazione) per sistemi dinamici, tenendo conto delle specifiche prestazioni richieste e delle limitazioni del sistema. Utilizzare metodi avanzati di analisi dei sistemi non lineari per prevedere e comprendere il comportamento del sistema in una vasta gamma di condizioni operative. Condurre esperimenti virtuali e simulazioni numeriche per valutare l'efficacia delle strategie di controllo proposte e confrontare le prestazioni con i sistemi lineari. Capacità critiche e di giudizio: Lo studente sarà in grado di valutare criticamente le limitazioni delle approssimazioni lineari nell'analisi e nel controllo dei sistemi non lineari e identificare situazioni in cui tali approssimazioni possono portare a risultati inaccurati o inadeguati. Inoltre, saprà analizzare in modo critico i risultati delle simulazioni e dei test sperimentali per valutare l'efficacia delle strategie di controllo proposte e identificare possibili miglioramenti. Infine, potrà valutare l'applicabilità delle soluzioni di controllo proposte in contesti ingegneristici reali, considerando vincoli di implementazione, costi e risorse disponibili. Sarà, infine, in grado di leggere in maniera critica articoli scientifici. Capacità comunicative: Lo studente sarà in grado di comunicare in modo chiaro e efficace i concetti teorici e le metodologie di progettazione relative ai sistemi non lineari e al controllo, sia verbalmente che per iscritto. Saprà presentare in modo chiaro e convincente i risultati delle analisi e delle simulazioni attraverso relazioni tecniche, presentazioni orali e documenti tecnici. Potrà collaborare in modo efficace con altri studenti e professionisti nel contesto del progetto e dell'implementazione di soluzioni di controllo per sistemi non lineari, comunicando in modo chiaro e conciso le proprie idee e opinioni. Capacità di apprendimento: Le modalità di svolgimento del corso mirano a sviluppare la capacità di saper comprendere metodi diversi, eventualmente elaborarne di individuali, nella soluzione dei problemi di analisi e controllo allo studio.
Nonlinear control systems design [ING-INF/04] [ITA]	1st	2nd	6
Educational objectives General objectives The course presents the basic methodologies for studying the properties of processes described by a nonlinear model and designing ad-hoc control laws for enforcing a desired behavior. The models at hand are described by a system of differential equations, assumed affine in the control. These models are successfully used to describe a variety of processes of interest in the engineering domain. Specific objectives Knowledge and understanding: The student will be able to understand the fundamental concepts for the analysis of nonlinear systems and control, including stability concepts and those that make use of differential geometry. The student will be able to distinguish the dynamic behaviors of linear and nonlinear systems. They will know the main control techniques for nonlinear systems to solve various problems such as stabilization, regulation, and tracking. Apply knowledge and understanding: Apply the acquired knowledge to design and implement effective controllers for nonlinear systems, taking into account the specific required performance and system limitations. Utilize advanced methods of nonlinear systems analysis to predict and understand the system's behavior under a wide range of operating conditions. Conduct virtual experiments and numerical simulations to assess the effectiveness of proposed control strategies and compare performance with linear systems. Critical and judgment skills: The student will be able to critically evaluate the limitations of linear approximations in the analysis and control of nonlinear systems and identify situations where such approximations may lead to inaccurate or inadequate results. Additionally, they will be able to critically analyze the results of simulations and experimental tests to assess the effectiveness of proposed control strategies and identify potential improvements. Finally, they will be able to evaluate the applicability of proposed control solutions in real engineering contexts, considering implementation constraints, costs, and available resources. They will also be able to critically read scientific articles. Communication skills: The student will be able to communicate theoretical concepts and design methodologies related to nonlinear systems and control clearly and effectively, both verbally and in writing. They will know how to present analysis and simulation results convincingly through technical reports, oral presentations, and technical documents. They will be able to collaborate effectively with other students and professionals in the context of designing and implementing control solutions for nonlinear systems, communicating their ideas and opinions clearly and concisely. Learning ability: The course methods aim to develop the ability to understand different methods, possibly devising individual ones, in solving problems related to analysis and control under study.
10596148 \| Filtering and Optimal Control [ING-INF/04] [ENG]	1st	2nd	12
Educational objectives General goals The course illustrates the methodologies of optimal control and filtering. The student will be able to formulate, analyze, and search for solutions of estimation, filtering and optimization problems of different nature. Specific outcomes - Knowledge and understanding The Students will lean the methods of filtering and optimal control theory and the main applications in different fields. - Capability to apply knowledge and understanding The Students will be able to propose the optimal control and estimation strategies depending on the specific problem at hand. - Critical and judgment skill The Students will be able to analyze control problems, proposing and implementing optimal control strategies. - Communication skills The course activities will allow the Students to increase their communication capabilities in scientific fields. - Learning skills One of the aim of the course is to increase the critical skills of the Students; they will be able to tackle a control problem, starting from the literature analysis, to the optimal control and filtering aspects, up to the implementation.
Module I [ING-INF/04] [ENG]	1st	2nd	6
Educational objectives General goals (Mod. I) The course illustrates the methodologies of optimal control. The student will be able to formulate, analyze, and search for solutions of optimization problems of different nature by an appropriate use of optimality conditions, with emphasis on optimal control problems. Specific outcomes - Knowledge and understanding The Students will lean the methods of optimal control theory and the main applications in different fields. - Capability to apply knowledge and understanding The Students will be able to propose the optimal control strategies depending on the specific problem at hand. - Critical and judgment skill The Students will be able to analyze control problems, proposing and implementing optimal control strategies. - Communication skills The course activities will allow the Students to increase their communication capabilities in scientific fields. - Learning skills One of the aim of the course is to increase the critical skills of the Students; they will be able to tackle a control problem, starting from the literature analysis, to the optimal control aspects, up to the implementation.
Module II [ING-INF/04] [ENG]	1st	2nd	6
Educational objectives General goals (Mod. I) The course illustrates the methodologies of optimal control. The student will be able to formulate, analyze, and search for solutions of optimization problems of different nature by an appropriate use of optimality conditions, with emphasis on optimal control problems. Specific outcomes - Knowledge and understanding The Students will lean the methods of optimal control theory and the main applications in different fields. - Capability to apply knowledge and understanding The Students will be able to propose the optimal control strategies depending on the specific problem at hand. - Critical and judgment skill The Students will be able to analyze control problems, proposing and implementing optimal control strategies. - Communication skills The course activities will allow the Students to increase their communication capabilities in scientific fields. - Learning skills One of the aim of the course is to increase the critical skills of the Students; they will be able to tackle a control problem, starting from the literature analysis, to the optimal control aspects, up to the implementation.
[N/D] [ENG]	1st	2nd	6
Optional group:
Optional group:
[N/D] [ENG]	2nd	1st	6
AAF1041 \| Training [N/D] [ENG]	2nd	2nd	3
AAF1025 \| Final exam [N/D] [ENG]	2nd	2nd	27
Optional group:
Optional group:

Lesson [SSD] [Language]	Year	Semester	CFU
1041422 \| [ING-INF/04] [ENG]	1st	1st	6
Educational objectives General objectives The course aims at providing basic concepts and methodologies related to the most widely used control methodologies in the framework of process automation and at applying them in the industrial contexts, suitably modeled as complex and heterogeneous processes that are interconnected through appropriate material transport and communication infrastructures. Specific objectives Knowledge and understanding: The students will learn methodologies for the robust control of linear time-delay systems, Internal Model Control and Model Predictive Control with specific reference to process control problems. Apply knowledge and understanding: Students will be able to design robust controllers for process automation equipment, e.g., to achieve robust tuning of PID controllers, and to apply industrial Model Predictive Control algorithms. Critical and judgment skills: The student will be able to choose the most suitable control methodology for a specific process control problem starting from its state-space model or from its transfer-function model. Communication skills: The course activities allow the student to be able to communicate and discuss the main problems concerning process control and the possible design choices for their solutions in terms of control laws. Learning ability: The aim of the course is to make the students aware on how to deal with control problems in the context of process automation.
1023235 \| [ING-INF/04] [ENG]	1st	1st	6
Educational objectives General objectives The course provides basic tools for the control of robotic systems: kinematic analysis, trajectory planning, programming of motion tasks for robot manipulators in industrial and service environments. Specific objectives Knowledge and understanding: Students will learn how actuation units and sensing components of robots operate, the basic methods for the kinematic modeling, analysis and control of robot manipulators, as well as the main algorithms for trajectory planning. Apply knowledge and understanding: Students will be able to analyze the kinematic structures of industrial robots and to design algorithms and modules for planning and controlling robot trajectories. Critical and judgment skills: Students will be able to characterize the functionality of a robotic system with reference to a given industrial or service task, analyzing the complexity of the solution, its performance, and the possible weaknesses. Communication skills: The course will allow students to be able to present the main problems and the technical solutions related to the use and application of robotic systems. Learning ability: The course aims at developing autonomous learning abilities in the students, oriented to the analysis and solution of problems in the use of robots.
1041453 \| [ING-INF/04] [ENG]	1st	2nd	6
Educational objectives General objectives The course presents advanced synthesis methods for the robust stabilization of linear multivariable and nonlinear systems in the presence of model uncertainties. Specific objectives Knowledge and understanding: Students will learn control design methods that handle the presence of structured or unstructured uncertainties on the model of the controlled system. The presented robust stabilization techniques will be based on the use of linear matrix inequalities (LMI) or methods based on high-gain controllers. Apply knowledge and understanding: Students will be able to analyze robust stabilization problems for linear and nonlinear dynamical systems and to use advanced design techniques in the synthesis of control laws for their resolution. Critical and judgment skills: Students will be able to characterize structured and/or unstructured uncertainties of the considered system, and to analyze the complexity of control law implementations, of their performance and critical issues. Communication skills: The course enables students to present some advanced and robust methods of solution for the classical problem of stabilization through feedback of dynamic systems. Learning ability: The course aims to create autonomous learning attitudes for the analysis and solution of control problems for linear multi-variable and nonlinear systems subject to model uncertainties.
1041426 \| [ING-INF/04] [ENG]	1st	2nd	6
Educational objectives General objectives Analyze the performance limitations of a control system. Learn the specificities of multivariable systems. Formulate control problem with closed loop specifications in order to achieve robust stability and performance. Specific objectives Knowledge and understanding: Students will learn (1) the tools for evaluating the performance limitations and (2) to formulate in the frequency domain robust control problems for multivariable systems. Apply knowledge and understanding: Students will be able to analyze and design multivariable control systems. Critical and judgment skills: Students will be able to evaluate the attainable performance of a control system. Communication skills: The course activities will allow students to be able to communicate/share the main problems concerning multivariable control systems. Learning ability: The course development aims at giving the student the capacity to design complex control systems in a linear setting.
1021883 \| [ING-INF/04] [ENG]	1st	2nd	6
Educational objectives General objectives The course provides advanced tools for the control of robotic systems: use of kinematic redundancy, dynamic modeling of robot manipulators, design of feedback control laws for free motion and interaction tasks, including visual servoing. Specific objectives Knowledge and understanding: Students will learn the methods for the dynamic modelling of manipulators, for the use of kinematic redundancy, as well as how control laws can be designed to execute robotic tasks in free motion or involving interaction with the environment. Apply knowledge and understanding: Students will be able to analyze the robot dynamics and to design algorithms and modules for controlling robot trajectories and contact forces with the environment. Critical and judgment skills: Students will be able to characterize the dynamic functionality of a robotic system with reference to a given task, analyzing the complexity of the solution, its performance, and the possible weaknesses. Communication skills: The course will allow students to be able to present the advanced problems and related technical solutions when using robots in dynamic conditions. Learning ability: The course aims at developing autonomous learning abilities in the students, oriented to the analysis and solution of advanced problems in the use of robots.
1022775 \| AUTONOMOUS AND MOBILE ROBOTICS [ING-INF/04] [ENG]	2nd	1st	6
Educational objectives General objectives The course presents the basic methods for designing and controlling autonomous mobile robots. Specific objectives Knowledge and understanding: Students will learn (1) basic methods for the modeling, analysis and control of mobile robots, both wheeled and legged, and (2) fundamental algorithms for autonomous motion planning. Apply knowledge and understanding: Students will be able to analyze and design architectures, algorithms and modules for planning, control and localization of autonomous mobile robots. Critical and judgment skills: Students will be able to choose the most suitable functional control architecture for a specific robotic system and to analyze its complexity as well as possible weaknesses. Communication skills: The course activities will allow students to be able to communicate/share the main problems concerning autonomous mobile robots, as well as the possible design choices for the control of such systems. Learning ability: The course development aim at giving the student a mindset oriented to the development of modules for the autonomous mobility of robots.
1041429 \| [ING-INF/04] [ENG]	2nd	1st	6
Educational objectives General objectives The course aims at applying advanced dynamic control methodologies to networks/systems by adopting a technologically independent abstract approach that addresses the problem of network/system control, leaving aside specific network/system technologies. Students will be able to design control actions suitable for communication, energy, transport, security and health networks/systems Specific objectives Knowledge and understanding: Students will be able to understand the specificity of some application environments such as those of communication, energy, transport, security and health networks/systems, as well as to abstractly model and control these networks/systems. Furthermore, if the modeling of such networks/systems is impossible or too complex to implement, students will be able to use data-driven techniques capable of combining control methodologies with artificial intelligence/machine learning methodologies. Apply knowledge and understanding: Students will be aware of the main problems and able to design control actions applicable to communication, energy, transport, safety and health networks/systems aimed at satisfying assigned design specifications.. Critical and judgment skills: Students will be able to choose the most suitable control methodologies for specific problems and to evaluate the complexity of the proposed solutions. Communication skills: The course activities allow the students to be able to communicate/share (i) the main problems relating to communication, energy, transport, safety, health networks/systems, (ii) possible design choices for the control of such networks/systems . Furthermore, the course includes the possibility of carrying out application theses on topics related to projects carried out by the research group coordinated by the teacher; as part of these activities, students will acquire the ability to collaborate in groups. Learning ability: The course development methods aim to create a mindset of the student oriented to the control of complex systems/networks, by appropriately combining methodologies coming from the automation field and from various other engineering areas.
1041428 \| [ING-INF/04] [ENG]	1st	1st	6
Educational objectives General objectives The course provides methodologies for the analysis of linear and nonlinear discrete time and sampled dynamics, the design of digital controllers with a major focus on linear systems, and implementation on embedded microcontrollers. The student will be able to compute digital models of given discrete time systems as well as digital discrete time equivalent models of continuous dynamics, to design digital control laws both for discrete and for continuous systems and to use standard microcontrollers for their implementation. Specific objectives Analysis and design techniques for discrete time and digital systems. Knowledge and understanding: The course provides methodologies for the analysis of linear and nonlinear discrete time and sampled dynamics, and for the design of digital controllers with a major focus on linear systems. Apply knowledge and understanding: The student will be able to compute digital models of given discrete time systems as well as digital discrete time equivalent models of continuous dynamics, to design digital control laws both for discrete and for continuous systems. Critical and judgment skills: The student will be able to choose between different methodologies, in order to solve the given problem in the best way. Communication skills: At the end of the course the student will be able to motivate his/her own design choices. Learning ability: The student will learn to develop independent studies by him/herself.
1041454 \| [ING-IND/32] [ENG]	2nd	1st	6
Educational objectives The course aims to guide the student in the understanding of the principles of operation of electrical drives and their components. The course provides the tools for analyzing the behavior of an electrical drive at steady state and during transients. The course is completed by some design fundamentals. At the end of the course the student will be able to understand the principle of operation and analyze the behavior of an electrical drive both at steady state and during transients. The acquired knowledge will allow to addressing design and control issues of electrical drives.
1041431 \| [ING-IND/13] [ENG]	2nd	2nd	6
Educational objectives General objectives The course aims to provide the student with a unitary theory for the study of vehicles in general, with particular reference to land vehicles. The analysis of the vehicle system is addressed both for component subsystems (i) rigid body dynamics, (ii) propulsion system (iii) transmission system (iv) directional control system (v) suspension system (vi) braking system (vii) driving and control automation systems, and in global terms, integrating all subsystems within a single model capable of describing complex maneuvers of the vehicle system. Specific objectives Knowledge and understanding: The student will learn the basic methods for modeling, analysis and control of vehicles. In the first part of the course, the notions concerning the dynamics of the vehicle in general will be provided, while in the second part, particular attention is paid to the mechanical, sensor and hardware subsystems in use. Applying knowledge and understanding: The student will be able to analyze and design different architectures of land vehicles. They will also have sufficient knowledge to choose the most suitable control algorithms to be used in the case of self-driving vehicles. Critical and judgement skills: The student will be able to choose the most suitable modeling methodology for the specific problem, and to examine an innovative device in the field of vehicle dynamics, understanding its operating principles and carrying out a feasibility analysis. Communication skills: The activities of the course, and specifically the development of the project of the year and its presentation of the project in the final exam, allow the student to be able to communicate/share the main innovative ideas present in a technological project and to synthesize in a clear and effective presentation the main contents. Learning skills: The student will be able to deal with a problem of design synthesis thanks to the exam modality. The student, suitably guided, puts into practice the techniques of "problem solving", i.e. the set of processes designed to analyze, address and solve a specific problem on the basis of the examination of patents or recent publications.
1041427 \| Control of Autonomous Multi-Agent Systems [ING-INF/04] [ENG]	2nd	2nd	6
Educational objectives General objectives This course deals with modeling, analysis and control of multi-agent systems, with emphasis on communication/distribution networks and multi-robot systems. Specific objectives Knowledge and understanding: Students will learn basic methods for the modeling, analysis and control of multi-agent systems, with particular attention to distributed control strategies. In the first part of the course, applications relating to communication, energy and health networks/systems will be presented; in the second part, multi-robot systems will be studied, both terrestrial and aerial. Apply knowledge and understanding: Students will be able to analyze and design architectures and algorithms for the control of multi-agent systems in various application fields. Critical and judgment skills: Students will be able to choose the most suitable control methodology for a specific problem and to evaluate the complexity of the proposed solution. Communication skills: The course activities allow the student to be able to communicate/share the main problems concerning networks and systems presented in the course, as well as the possible design choices for the control of such networks/systems. Learning ability: The course aims at giving the students a mindset oriented to the control of complex systems/networks by appropriately combining methodologies coming from the control theory as well as from other engineering disciplines. Furthermore, the course includes the possibility of carrying out application theses on topics related to projects carried out by the research group coordinated by the teachers; as part of these activities, students will acquire the ability to collaborate in groups.
10592976 \| Advanced methods in control [ING-INF/04] [ENG]	2nd	2nd	6
Educational objectives Objectives General Objectives: This course introduces students to energy-based modeling and control methods for lumped-parameter multi-physical systems (electrical, mechanical, thermodynamical), focusing on: 1) port-based modeling using bond graphs; 2) energy-based modeling and port-Hamiltonian control methods; and 3) an introduction to geometric control on Lie groups. Specific Objectives: Knowledge and Understanding: Students will expand their knowledge and understanding of linear algebra and differential geometry. including concepts such as dual spaces, tensors, manifolds and tangent spaces. They will acquire knowledge beyond standard signal-based system modeling. The students will understand the bottom-up modeling of systems using the concept of power ports and how to classify atomic subsystems based on energy, regardless of the specific physical domain. They will learn about Dirac structures and how to derive the port-Hamiltonian model of a system. The students will also learn key control design methods using the port-Hamiltonian structure of systems, such as energy shaping, energy balancing, passivity-based control, and IDA-PBC. Finally, they will learn the basics of Lie groups and the energetic modeling of rigid bodies, along with some key geometric control strategies for controlling single and multi-body systems. Applying Knowledge and Understanding: The students will apply their knowledge and understanding to the modeling and analysis of multi-physical systems from an energy-based perspective using bond graphs. They will also apply this knowledge to the synthesis of controllers using key energy-based control methods and geometric control techniques. Critical and Judgment Skills: The students will learn how to represent and analyze content related to the modeling and control of multi-physical systems. The course will improve their critical and analytical capabilities by using visual representations, such as bond graphs, to illustrate system dynamics, and by employing coordinate-free descriptions of systems and controller synthesis. Communication Skills: The course will equip students with the ability to present and discuss technical problems and solutions related to port-based modeling and geometric control, using advanced mathematical and visual tools. Learning Ability: The course promotes independent learning by encouraging students to engage with theoretical foundations, analyze scientific literature, and implement advanced control strategies in real-world scenarios.
10606939 \| Intelligent and Hybrid Control [ING-INF/04] [ENG]	1st	2nd	6
Educational objectives General objectives: The main objective of the course is the acquisition and use by the student of the basic tools necessary for the construction and analysis of intelligent and hybrid control systems for phenomena of interest in automation engineering with reference to both data-driven and model-based methodologies. Specific objectives Knowledge and understanding: The course provides advanced tools for the analysis and design of complex systems that combine different types of technologies or behaviors to achieve a desired closed-loop outcome and performance. The systems considered will be: - intelligent control systems that integrate neural networks and data-driven algorithms for machine learning and data/feedback analysis - dynamic models that integrate time-based dynamic behaviors (modeled by differential equations) with event-based dynamic behaviors (modeled by automata). Apply knowledge and understanding: The student will learn how to independently apply the methodologies and techniques presented in the course to the design and development of complex control systems integrating machine learning tools and event-based modeling. The student will be able to identify and model hybrid and nonlinear dynamics through both data-driven and model-based approaches. Critical and judgment skills: The student will be able to determine which approaches are best suited to the development of predictive models to represent complex systems, combining machine learning techniques with general modeling approaches such as automata and switching dynamics. The student will also be able to critically evaluate the most critical closed-loop performance and properties for the design of intelligent control laws. Communication skills: The student will be able to present and analyze complex dynamical systems and related hybrid and intelligent controllers in the context of industrial applications and process automation. Learning ability: The course aims to provide students with all the elements for autonomous learning aimed at the analysis and design of advanced control systems and integrating machine learning capabilities in all areas of interest for automation engineering.

Lesson [SSD] [Language]	Year	Semester	CFU
10592834 \| Neuroengineering [ING-INF/06] [ENG]	1st	2nd	6
Educational objectives * General objectives The course aims to introduce the principles, methodologies, and applications of the main engineering techniques used to study and interact with neural systems. * Specific objectives - Knowledge and understanding Students will learn the basics of the human brain functioning and organization at different scales, and to the main applications of engineering and information technologies to neuroscience - Applying knowledge and understanding Students will familiarize with basic tools to utilize to acquire, process and decode neurophysiological and muscular signals and to interface them with artificial devices - Critical and judgment skills Students will learn how to choose the most suitable control methodology for a specific problem and to evaluate the complexity of the proposed solution. - Communication skills Students will learn to communicate in a multidisciplinary context the main issues of interfacing neurophysiological signals with artificial systems, and to convey possible design choices for this purpose. - Learning ability Students will develop a mindset oriented to independent learning of advanced concepts not covered in the course.
1021883 \| [ING-INF/04] [ENG]	1st	2nd	6
Educational objectives General objectives The course provides advanced tools for the control of robotic systems: use of kinematic redundancy, dynamic modeling of robot manipulators, design of feedback control laws for free motion and interaction tasks, including visual servoing. Specific objectives Knowledge and understanding: Students will learn the methods for the dynamic modelling of manipulators, for the use of kinematic redundancy, as well as how control laws can be designed to execute robotic tasks in free motion or involving interaction with the environment. Apply knowledge and understanding: Students will be able to analyze the robot dynamics and to design algorithms and modules for controlling robot trajectories and contact forces with the environment. Critical and judgment skills: Students will be able to characterize the dynamic functionality of a robotic system with reference to a given task, analyzing the complexity of the solution, its performance, and the possible weaknesses. Communication skills: The course will allow students to be able to present the advanced problems and related technical solutions when using robots in dynamic conditions. Learning ability: The course aims at developing autonomous learning abilities in the students, oriented to the analysis and solution of advanced problems in the use of robots.
1054963 \| Systems and Control Methods for Cyber-Physical Security [ING-INF/04] [ENG]	1st	2nd	6
Educational objectives General Objectives. The course introduces to the modeling and analysis of cyber-physical systems subject to attacks, mainly using concepts and methods from control theory and risk management (concepts and methods will be recalled for completeness). It is shown how it is possible to design sophisticated attacks capable of disrupting a cyber-physical control system, bypassing the detection and protection mechanisms of the system, and producing degradation of service or even physical damage to the system. Relevant types of cyber-physical attacks (false data injection, denial of service, replay attack, zero dynamics attack, covert attack, etc.) are studied, by mathematically modeling them and analyzing their working principle, also by making use of computer simulations. Theoretical results to determine whether a given cyber-physical system may be subject to undetectable attacks will be presented. Basic methodologies for detecting attacks, and for mitigating them, are introduced. During the course, examples from different application fields are studied and discussed, particularly in the context of control systems and critical infrastructures (with special focus on smart grids). Specific Objectives. Knowledge and understanding: At the end of the course, the student will know the main methodologies for modeling and analyzing cyber-physical systems and the main types of cyber-physical attacks. The student will know and understand important theoretical results for analyzing the vulnerability of control systems to cyber-physical attacks, as well as methods for detection and mitigation of attacks. Apply knowledge and understanding: The student will be able to model a cyber-physical system and analyze its security properties. He/she will be able to model and analyze different attack scenarios, evaluating impacts and possible mitigation strategies. Critical and judgment skills: The student will be able to critically and quantitatively evaluate the security properties of cyber-physical control systems against different possible attack scenarios. He/she will be able to suggest strategies for improving the security of the system and for mitigating possible attacks. The student will be able to critically read and assimilate relevant technical documentation. Communication Skills: The student will be able to communicate clearly and effectively in relation to the main issues pertaining to the security of cyber-physical systems (modeling, analysis of attack scenarios, design of prevention and protection strategies, etc.). Learning ability: Through the direct study of scientific articles, and with an emphasis on the study of rational and systematic methods for dealing with cyber-security problems, the course will strengthen the students' ability to continue the study autonomously, in the industry or in the research.
1022863 \| MEDICAL ROBOTICS [ING-INF/04] [ENG]	1st	2nd	6
Educational objectives General objectives Introduction to the basic robotic technologies in the medical context, with particular emphasis on surgical robotics. Expected learning results: Knowledge of the main robotic surgical systems, of the challenges and methodologies of medical robot design and control. Specific objectives Knowledge and understanding The student will learn: to critically read articles that describe the main technologies involved in medical robotics; to discuss in detail the state of the art of robotic applications in medicine; how to approach the design of robot-assisted medical systems; robot modeling and control methodologies needed in the development of medical robotic systems. Apply knowledge and understanding The student will be able to design new robotic technologies for medical applications. In particular, he/she will be able to develop robotic simulation systems, to analyze, to model and to design control schemes for teleoperated medical robots and for the execution of tasks shared between humans and robots. Critical and judgment skills The student will be able to estimate the potential benefits deriving from the introduction of robotic support in a medical procedure and to evaluate the clinical, social and economic constraints in the implementation of robotic technology in a medical sector. Communication skills: The student will learn to communicate and collaborate with people of different backgrounds. Learning ability The student will be able to independently learn new concepts useful for the design and development of new technologies for medical applications.
10592976 \| Advanced methods in control [ING-INF/04] [ENG]	2nd	2nd	6
Educational objectives Objectives General Objectives: This course introduces students to energy-based modeling and control methods for lumped-parameter multi-physical systems (electrical, mechanical, thermodynamical), focusing on: 1) port-based modeling using bond graphs; 2) energy-based modeling and port-Hamiltonian control methods; and 3) an introduction to geometric control on Lie groups. Specific Objectives: Knowledge and Understanding: Students will expand their knowledge and understanding of linear algebra and differential geometry. including concepts such as dual spaces, tensors, manifolds and tangent spaces. They will acquire knowledge beyond standard signal-based system modeling. The students will understand the bottom-up modeling of systems using the concept of power ports and how to classify atomic subsystems based on energy, regardless of the specific physical domain. They will learn about Dirac structures and how to derive the port-Hamiltonian model of a system. The students will also learn key control design methods using the port-Hamiltonian structure of systems, such as energy shaping, energy balancing, passivity-based control, and IDA-PBC. Finally, they will learn the basics of Lie groups and the energetic modeling of rigid bodies, along with some key geometric control strategies for controlling single and multi-body systems. Applying Knowledge and Understanding: The students will apply their knowledge and understanding to the modeling and analysis of multi-physical systems from an energy-based perspective using bond graphs. They will also apply this knowledge to the synthesis of controllers using key energy-based control methods and geometric control techniques. Critical and Judgment Skills: The students will learn how to represent and analyze content related to the modeling and control of multi-physical systems. The course will improve their critical and analytical capabilities by using visual representations, such as bond graphs, to illustrate system dynamics, and by employing coordinate-free descriptions of systems and controller synthesis. Communication Skills: The course will equip students with the ability to present and discuss technical problems and solutions related to port-based modeling and geometric control, using advanced mathematical and visual tools. Learning Ability: The course promotes independent learning by encouraging students to engage with theoretical foundations, analyze scientific literature, and implement advanced control strategies in real-world scenarios.
1022775 \| [ING-INF/04] [ENG]	2nd	1st	6
Educational objectives General objectives The course presents the basic methods for designing and controlling autonomous mobile robots. Specific objectives Knowledge and understanding: Students will learn (1) basic methods for the modeling, analysis and control of mobile robots, both wheeled and legged, and (2) fundamental algorithms for autonomous motion planning. Apply knowledge and understanding: Students will be able to analyze and design architectures, algorithms and modules for planning, control and localization of autonomous mobile robots. Critical and judgment skills: Students will be able to choose the most suitable functional control architecture for a specific robotic system and to analyze its complexity as well as possible weaknesses. Communication skills: The course activities will allow students to be able to communicate/share the main problems concerning autonomous mobile robots, as well as the possible design choices for the control of such systems. Learning ability: The course development aim at giving the student a mindset oriented to the development of modules for the autonomous mobility of robots.
1022792 \| [ING-INF/05] [ENG]	2nd	1st	6
Educational objectives General objectives To provide the concepts necessary to: (a) understand the meaning of information security and security of infrastructures and networks; (b) enable the student to make analysis of the fundamental security features of networks and infrastructures; (c) provide the fundamental tools for the design and the assessment of the solutions implemented in the network for the information security requirements. Methodologies and notions include cryptography, access control, security protocols and architectures, firewalls. Specific objectives Capacity to - recognize the requirements of confidentiality, integrity, authenticity, authentication and non-repudiation during the analysis/design phase, identifying suitable standards to guarantee them; - support the process of analysis and definition of security policies at the organization level; - critically evaluate infrastructures and applications with respect to security requirements; - assess the presence of significant vulnerabilities in infrastructures and applications; - study and understand security standards. Knowledge and understanding Knowledge of the fundamentals of cryptography. Understanding of certification mechanisms and digital signature. Understanding of cyber threats arising from interaction with the web and the internet in general Apply knowledge and understanding To select and use effective and secure encryption standards. To select and use effective and secure document fingerprinting standards. To use digital signatures. To choose secure authentication mechanisms. Critical and judgment skills Being able to assess the adequacy of IT security measures employed by a small/medium enterprise. Communication skills Being able to easily and effectively interact with industrial and ICT domain specialists for all issues related to information security. Knowing how to motivate results of analyses and requirements. Learning ability: Ability to read and understand documents with technical standards and/or for the disclosure of new IT threats.
1041429 \| [ING-INF/04] [ENG]	2nd	1st	6
Educational objectives General objectives The course aims at applying advanced dynamic control methodologies to networks/systems by adopting a technologically independent abstract approach that addresses the problem of network/system control, leaving aside specific network/system technologies. Students will be able to design control actions suitable for communication, energy, transport, security and health networks/systems Specific objectives Knowledge and understanding: Students will be able to understand the specificity of some application environments such as those of communication, energy, transport, security and health networks/systems, as well as to abstractly model and control these networks/systems. Furthermore, if the modeling of such networks/systems is impossible or too complex to implement, students will be able to use data-driven techniques capable of combining control methodologies with artificial intelligence/machine learning methodologies. Apply knowledge and understanding: Students will be aware of the main problems and able to design control actions applicable to communication, energy, transport, safety and health networks/systems aimed at satisfying assigned design specifications.. Critical and judgment skills: Students will be able to choose the most suitable control methodologies for specific problems and to evaluate the complexity of the proposed solutions. Communication skills: The course activities allow the students to be able to communicate/share (i) the main problems relating to communication, energy, transport, safety, health networks/systems, (ii) possible design choices for the control of such networks/systems . Furthermore, the course includes the possibility of carrying out application theses on topics related to projects carried out by the research group coordinated by the teacher; as part of these activities, students will acquire the ability to collaborate in groups. Learning ability: The course development methods aim to create a mindset of the student oriented to the control of complex systems/networks, by appropriately combining methodologies coming from the automation field and from various other engineering areas.
1055496 \| Control problems in robotics [ING-INF/04] [ENG]	2nd	1st	6
Educational objectives General objectives The course is composed of two modules, presents a selection of advanced topics in Robotics and is intended as an introduction to research. Guided through case studies taken from the research activities of the teachers, the student will be able to fully develop a problem in Robotics, from its analysis to the proposal of solution methods and their implementation. Specific objectives Knowledge and understanding: Students will learn some advanced control techniques used in some robotic research areas where the lecturers are active. Apply knowledge and understanding: Students will be able to use and design complex control systems for advanced robotic problems. Critical and judgment skills: Students will be able to evaluate some methodologies used in the difference robotic applied illustrated areas. Communication skills: The course activities will allow students to be able to communicate and share the different solutions, adopted in a research framework, for the different illustrated robotic areas. Learning ability: The course development aims at giving the student the capacity to design complex control systems for advanced robotic systems.
1041428 \| [ING-INF/04] [ENG]	2nd	1st	6
Educational objectives General objectives The course provides methodologies for the analysis of linear and nonlinear discrete time and sampled dynamics, the design of digital controllers with a major focus on linear systems, and implementation on embedded microcontrollers. The student will be able to compute digital models of given discrete time systems as well as digital discrete time equivalent models of continuous dynamics, to design digital control laws both for discrete and for continuous systems and to use standard microcontrollers for their implementation. Specific objectives Analysis and design techniques for discrete time and digital systems. Knowledge and understanding: The course provides methodologies for the analysis of linear and nonlinear discrete time and sampled dynamics, and for the design of digital controllers with a major focus on linear systems. Apply knowledge and understanding: The student will be able to compute digital models of given discrete time systems as well as digital discrete time equivalent models of continuous dynamics, to design digital control laws both for discrete and for continuous systems. Critical and judgment skills: The student will be able to choose between different methodologies, in order to solve the given problem in the best way. Communication skills: At the end of the course the student will be able to motivate his/her own design choices. Learning ability: The student will learn to develop independent studies by him/herself.
1022858 \| [ING-INF/05] [ENG]	2nd	1st	6
Educational objectives General Objectives: The objectives of this course are to present a wide spectrum of Machine Learning methods and algorithms, discuss their properties, convergence criteria and applicability. The course will also present examples of successful application of Machine Learning algorithms in different application scenarios. The main outcome of the course is the capability of the students of solving learning problems, by a proper formulation of the problem, a proper choice of the algorithm suitable to solve the problem and the execution of experimental analysis to evaluate the results obtained. Specific Objectives: Knowledge and understanding: Providing a wide overview of the main machine learning methods and algorithms for the classification, regression, unsupervised learning and reinforcement learning problems. All the problems are formally defined and theoretical basis as well as technical and implementation details are provided in order to understand the proposed solutions. Applying knowledge and understanding: Solving specific machine learning problems starting from training data, through a proper application of the studied methods and algorithms. The development of two homeworks (small projects to be developed at home) allows the students to apply the acquired knowledge. Making judgements: Ability of evaluating performance of a machine learning system using proper metrics and evaluation methodologies. Communication skills: Ability of writing a technical report describing the results of the homeworks, thus showing abilities in communicating results obtained from the application of the acquired knowledge in solving a specific problem. Being exposed to examples of communication of results obtained in practical cases given by experts within seminars offered during the course. Learning skills: Self-study of specific application domains, problems and solutions during the homeworks, with possible application of teamwork for the solution of specific problems.
10606939 \| Intelligent and Hybrid Control [ING-INF/04] [ENG]	1st	2nd	6
Educational objectives General objectives: The main objective of the course is the acquisition and use by the student of the basic tools necessary for the construction and analysis of intelligent and hybrid control systems for phenomena of interest in automation engineering with reference to both data-driven and model-based methodologies. Specific objectives Knowledge and understanding: The course provides advanced tools for the analysis and design of complex systems that combine different types of technologies or behaviors to achieve a desired closed-loop outcome and performance. The systems considered will be: - intelligent control systems that integrate neural networks and data-driven algorithms for machine learning and data/feedback analysis - dynamic models that integrate time-based dynamic behaviors (modeled by differential equations) with event-based dynamic behaviors (modeled by automata). Apply knowledge and understanding: The student will learn how to independently apply the methodologies and techniques presented in the course to the design and development of complex control systems integrating machine learning tools and event-based modeling. The student will be able to identify and model hybrid and nonlinear dynamics through both data-driven and model-based approaches. Critical and judgment skills: The student will be able to determine which approaches are best suited to the development of predictive models to represent complex systems, combining machine learning techniques with general modeling approaches such as automata and switching dynamics. The student will also be able to critically evaluate the most critical closed-loop performance and properties for the design of intelligent control laws. Communication skills: The student will be able to present and analyze complex dynamical systems and related hybrid and intelligent controllers in the context of industrial applications and process automation. Learning ability: The course aims to provide students with all the elements for autonomous learning aimed at the analysis and design of advanced control systems and integrating machine learning capabilities in all areas of interest for automation engineering.
1041427 \| [ING-INF/04] [ENG]	2nd	2nd	6
Educational objectives General objectives This course deals with modeling, analysis and control of multi-agent systems, with emphasis on communication/distribution networks and multi-robot systems. Specific objectives Knowledge and understanding: Students will learn basic methods for the modeling, analysis and control of multi-agent systems, with particular attention to distributed control strategies. In the first part of the course, applications relating to communication, energy and health networks/systems will be presented; in the second part, multi-robot systems will be studied, both terrestrial and aerial. Apply knowledge and understanding: Students will be able to analyze and design architectures and algorithms for the control of multi-agent systems in various application fields. Critical and judgment skills: Students will be able to choose the most suitable control methodology for a specific problem and to evaluate the complexity of the proposed solution. Communication skills: The course activities allow the student to be able to communicate/share the main problems concerning networks and systems presented in the course, as well as the possible design choices for the control of such networks/systems. Learning ability: The course aims at giving the students a mindset oriented to the control of complex systems/networks by appropriately combining methodologies coming from the control theory as well as from other engineering disciplines. Furthermore, the course includes the possibility of carrying out application theses on topics related to projects carried out by the research group coordinated by the teachers; as part of these activities, students will acquire the ability to collaborate in groups.

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Curriculum(s) for 2024 - Control Engineering (29933)