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Cyber-Physical Control Systems

Objectives

In this curricular unit the students will have a broad perspective of the methods and architectures of decision and control for cyber-physical systems, understanding their potential and limitations. Simultaneously, the students will have an experience of design and implementation of control strategies that can offer solutions to a concrete problem.

To this end, the intended learning outcomes for this curricular unit are the following:
OA1. Analyze and model complex dynamical systems in order to apply the addressed control strategies;
OA2. Understand and design model-based predictive controllers;
OA3. Understand the different control architectures and implications of using communication networks for control;
OA4. Develop solutions to concrete decision and control problems in cyber-physical systems.

General characterization

Code

12706

Credits

6.0

Responsible teacher

Daniel de Matos Silvestre

Hours

Weekly - 4

Total - 56

Teaching language

Português

Prerequisites

Available soon

Bibliography

Recommended:
- Class presentation slides, Bruno Guerreiro, 2022.
- Predictive Control for Linear and Hybrid Systems, Borrelli, Bemporad, Morari, Cambridge, 2017.
- Model Predictive Control: Theory, Comp., and Design, J. Rawlings, D. Mayne, M. Diehl, Nob Hill, 2017.

Additional:
- Exercises, Bruno Guerreiro, 2022.
- Project assignments, Bruno Guerreiro, 2022.
- Model Predictive Control System Design and Implementation Using MATLAB, Liuping Wang, Springer, 2009.
- Predictive control: with constraints, Jan Marian Maciejowski, Pearson education, 2002.
- Hybrid Systems: Modeling, Analysis and Control, J. Lygeros, C. Tomlin, and S. Sastry, 2008.
- MATLAB Primer: https://www.mathworks.com/help/pdf_doc/matlab/getstart.pdf
- Simulink User Guide: https://www.mathworks.com/help/pdf_doc/simulink/sl_using.pdf

Teaching method

Available soon

Evaluation method

Available soon

Subject matter

M0. Introduction and challenges in cyber-physical and energy systems
M1.1. Discrete-time systems state model representation
M1.2. Otimization and optimal control
M2.1. Introduction to the design of Model-based Predictive Control systems (MPC)
M2.2. Adding Constraints to the MPC design
M3.1. Distributed optimization and networked control
M3.2. Decentralized MPC design
M3.3. Distributed MPC design
M4.1. Modeling of hybrid systems
M4.2. MPC design for hybrod systems
M5.1. Nonlinear model-based predictive control (NMPC)
M5.2. Feasibility and stability analysis