| 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.
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| 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.
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| 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). Computer simulations are performed (using software such as Matlab, Python, Julia, Gurobi) to practically illustrate the concepts studied during the course.
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. He/She will be able to use the computer to perform relevant quantitative analyses through simulation.
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.
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| 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.
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| 10592976 | Advanced methods in control [ING-INF/04] [ENG] | 2nd | 2nd | 6 |
Educational objectives Objectives
General 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.
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| 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.
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| 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.
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| 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.
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| 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.
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| 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.
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| 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.
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| 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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| 10621505 | AGILE AND LEGGED ROBOTS [ING-INF/04] [ENG] | 2nd | 2nd | 6 |
Educational objectives General objectives
The course presents advanced analysis tools and control methods for the planning and control of robots which are expected to do agile motions. Underactuated robots will be analyzed in detail in order to understand how to exploit their internal structure. Particular emphasis will be given to legged robots (both quadrupeds and humanoids).
Specific objectives
Knowledge and understanding:
Students will learn control design methods and optimization based methods used for planning and control of underactuated robots.
Apply knowledge and understanding:
Students will be able to analyze the robot’s dynamics, understand its level of underactuation, use nonlinear control tools and optimization-based tools to analyse and control such systems.
Critical and judgment skills:
Students will be able to control underactuated systems as bipeds or quadrupeds.
Communication skills:
Interactive sessions for the design of controllers are planned.
Learning ability:
The course aims to create autonomous learning attitudes for the analysis and solution of control problems for underactuated robots such as bipeds and quadrupeds.
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| N/D | AGILE AND LEGGED ROBOTS - MODULE 1 [ING-INF/04] [ENG] | 2nd | 2nd | 3 |
Educational objectives General objectives
The course presents advanced analysis tools and control methods for the planning and control of robots which are expected to do agile motions. Underactuated robots will be analyzed in detail in order to understand how to exploit their internal structure. Particular emphasis will be given to legged robots (both quadrupeds and humanoids).
Specific objectives
Knowledge and understanding:
Students will learn control design methods and optimization based methods used for planning and control of underactuated robots.
Apply knowledge and understanding:
Students will be able to analyze the robot’s dynamics, understand its level of underactuation, use nonlinear control tools and optimization-based tools to analyse and control such systems.
Critical and judgment skills:
Students will be able to control underactuated systems as bipeds or quadrupeds.
Communication skills:
Interactive sessions for the design of controllers are planned.
Learning ability:
The course aims to create autonomous learning attitudes for the analysis and solution of control problems for underactuated robots such as bipeds and quadrupeds.
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