Cyber-Physical Control Systems


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





Responsible teacher

Daniel de Matos Silvestre


Weekly - 4

Total - 56

Teaching language



Students must have attended the courses Control Theory and Computer Control (or equivalent).


- 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.

- 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:
- Simulink User Guide:

Teaching method

The course is organized in theoretical-practical classes and laboratory classes. In the theoretical-practical classes the concepts are introduced and applied in concrete cases from an analytical point of view. In addition, the practical (or laboratory) classes are directed to the development of the techniques addressed in the theoretical classes applied to concrete cases, with the goal to obtain experimental results and their analysis.

The course may use a Blended Learning (B-Learning) methodology, where new contents are introduced asynchronously using Moodle, while the synchronous classes (either in person or online) are used to consolidate the contents, addressing students questions, and solving more complex problems. The use of active learning techniques will also be encouraged.

Evaluation method

The final grade (F) is defined as: F = 0.4*HW + 0.1*Q + 0.5*P
- Homeworks (HW): the theoretical-practical component will be primarily evaluated through individual homeworks, typically every other week;
- Online quizzes and other tools (Q): moodle short-quizzes and other online assessment tools, fundamental in the blended-learning model;
- Project assignments (P): two project assignments will be given to promote deeper levels of understanding of the course topics, applied to concrete scenarios.

The assessment components Homeworks (HW) and Online Quizzes (Q) are considered the theoretical-practical component, and as such, there is the final exam as an alternative. The Project assignments (P) will count as the laboratorial assessment grade.

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