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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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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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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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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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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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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