The objective of this second course is to provide the students with methods, tools, and algorithms for discrete-time control systems both in a deterministic and in a stochastic setting. Topics: discrete-time state-space description of deterministic and stochastic systems, design, and implementation of state estimation algorithms in a deterministic and in a stochastic framework, optimal and predictive control schemes, dynamic programming, distributed model-predictive control, networked control.
Modelling and Control of Cyber-Physical Systems II
Learning Goals
Program in pills
(1) State-space discrete-time systems theory: objectives and requirements of CPS control. (2) Stochastic state estimation from observed data. Kalman prediction and filtering. (3) Basic direct design of the controller. (4) Optimization-based control. (5) Discrete-time stochastic dynamic programming; linear-quadratic-Gaussian (LQG) control. (6). Model-predictive control (MPC): Explicit MPC. Decentralized and distributed MPC for networked CPS. Control for multi-agent systems. Industrial applications.
Area
Multidisciplinary, Ethical-Judicial-Social Knowledge and Applications
Curriculum Foundations
TAF Type
Curriculum Industry
TAF Type
Curriculum Health
TAF Type
Curriculum Economy
TAF Type
SSD
ECTS
Semester
Lecturers
Davide Raimondo