At the end of the course, the student will know the main paradigms of CPS and she/he will be able to design distributed estimation and control algorithms for basic CPS. Specifically, she/he will learn how to: - mathematically model CPS; - describe and estimate/predict the behavior of CPS; - control the behavior of CPS; - implement the theoretical notions in real-world CPS applications.

Linear Algebra, Linear system theory, Automatic Control, basic notions of Convex Optimization

- Introduction to CPS: general definition and motivations. Real-world examples. [0.3 CFU] - Mathematical modelization of CPS: linear time-invariant dynamic systems, hybrid dynamic systems, dynamic systems under adversarial attacks [0.4 CFU] - Secure state estimation of CPS under sensor attacks, through sparse optimization techniques [0.8 CFU] - Design of Luenberger-based observers for secure state estimation [0.4 CFU] - Introduction to distributed optimization: consensus algorithms and consensus-based distributed estimation techniques for CPS [0.6 CFU] - Real-world applications: the RSSI indoor localization problem [0.5 CFU] - Modeling of the physical process part of CPS by means of set-membership system identification techniques. Characterization of model uncertainty [0.5 CFU] - Review of optimal control theory for LTI systems and observer-based output feedback regulation of linear systems [0.3 CFU] - Cooperative control problem: general formulation, cooperative tracking, synchronization. [0.7 CFU] - Distributed control of multi-agents sytems: observer-based synchronization algorithms. [0.5 CFU] - Analysis of critical aspects of multi-agent dynamical systems: cyber and/or physical adversarial attacks, networked-induced delays and perturbations. [0.5 CFU] - Cooperative distributed vehicle formation control algorithms. [0.5 CFU]

The course consists of a theoretical part (mathematical modeling, optimization, theory of distributed systems) alternated with practical examples and implementation. For each theoretical concept, related real-world case studies are proposed (e.g., sensor networks, autonomous vehicles formation control, multi-agent consensus, multi-agent synchronization).

Since the considered topics are quite recent, main learning resources are: - Slides and lectures notes provided by the teachers - Selected scientific papers Supplementary material: - “Distributed Optimization for Smart Cyber-Physical Networks”, G. Notarstefano, I. Notarnicola, A. Camisa, Foundations and Trends in Systems and Control, 2019 - “Principles of Cyber-Physical Systems”, R. Alur, MIT Press, 2015 - “Cyber-Physical Systems: Foundations, Principles and Applications” - AP Elsevier, 2016 - “Cyber-Physical Systems: From Theory to Practice”, D. B. Rawat, J. J.P.C. Rodrigues, I. Stojmenovic, 1st ed. 2016, CRC Press - “Introduction to Embedded Systems - A Cyber-Physical Systems Approach”, E. A. Lee and S. A. Seshia, MIT Press, 2016 - “Cooperative Control of Multi-Agent Systems”, Lewis, F.L., Zhang, H., Hengster-Movric, K., Das, A., Springer, 2014

Lecture slides; Exercises; Exercise with solutions ; Lab exercises; Lab exercises with solutions;

Exam: Compulsory oral exam; Group project;

The exam consists of two parts. The first part is an oral interview where students are required to answer questions about the entire content of the course. The duration of the oral interview is about 1 hour; students are not allowed to use neither books nor lecture notes. The oral interview mainly focuses on the theoretical aspects of the course. The maximum score for this part is 20 points. In the second part a group project activity is assigned in order to check the ability of the students to apply the tools and results presented in the lectures to real-world case studies. The maximum score for this part is 10 points. The development of the assigned project requires application to practical problems of the theory and algorithms discussed in the entire course. By combining the two parts of the exam, the teachers assess the achievement of all the expected learning outcomes.

In addition to the message sent by the online system, students with disabilities or Specific Learning Disorders (SLD) are invited to directly inform the professor in charge of the course about the special arrangements for the exam that have been agreed with the Special Needs Unit. The professor has to be informed at least one week before the beginning of the examination session in order to provide students with the most suitable arrangements for each specific type of exam.