Control and Optimization
| Code: | M.EEC008 | Acronym: | COTI |
| Keywords | |
|---|---|
| Classification | Keyword |
| OFICIAL | Automation and Control |
Instance: 2021/2022 - 2S 
| Active? | Yes |
| Responsible unit: | Department of Electrical and Computer Engineering |
| Course/CS Responsible: | Master in Electrical and Computer Engineering |
Cycles of Study/Courses
| Acronym | No. of Students | Study Plan | Curricular Years | Credits UCN | Credits ECTS | Contact hours | Total Time |
|---|---|---|---|---|---|---|---|
| M.EEC | 32 | Syllabus | 1 | - | 6 | 39 |
Teaching language
EnglishObjectives
The aim of this curricular unit is to provide to the students a solid background of tools and methodologies necessary to understand and design dynamic control systems with particular emphasis on non-linear systems using nonlinear and optimization-based control techniques. These concepts are fundamental in various application areas, such as in automation and robotics, in cyber-physical systems, and in general in decision and control systems.
Learning outcomes and competences
Students after this course unit should be able to understand the main differences between linear and nonlinear systems, use Lyapunov concepts to characterize the stability of a dynamic system, know and apply the main techniques of nonlinear control and optimal control design.
Working method
PresencialProgram
1. Linear and nonlinear dynamical systems (equilibrium points, phase portrait, limit cycles)
2. Linearization (around an equilibrium point and around a trajectory)
3. Lyapunov Stability (Linearized systems and nonlinear systems)
4. Nonlinear control design (sliding-mode and Lyapunov based control)
5. Introduction to optimal control (Basic concepts and optimality conditions)
6. The Linear Quadratic Regulator (LQR)
7. Model Predictive Control (MPC) (linear and nonlinear systems)
Mandatory literature
Khalil, H. K., & Grizzle, J. W. ; Nonlinear systems (Vol. 3), Upper Saddle River, NJ: Prentice hall, 2002Complementary Bibliography
Slotine, J. J. E., & Li, W.; Applied nonlinear control, Englewood Cliffs, NJ: Prentice hall., 1991Sastry, S.; Nonlinear systems: analysis, stability, and control, Springer Science & Business Media, 2013
Vincent, T. L., & Grantham, W. J.; Nonlinear and optimal control systems, John Wiley & Sons., 1997
Rawlings, J. B., & Mayne, D. Q.; Model predictive control: Theory and design., 2009
Comments from the literature
Some study material consists of scientific articles and other publications that will be indicated by the Professor when appropriate.Teaching methods and learning activities
Theoretical classes will consist of exposition of the contents and discussion of illustrative examples of the concepts presented. Theoretical-practical classes are focused on application work (mini-projects of control and optimization) and practical exercises supported by the use of computational tools.Software
MatlabEvaluation Type
Distributed evaluation without final examAssessment Components
| Designation | Weight (%) |
|---|---|
| Trabalho prático ou de projeto | 30,00 |
| Teste | 70,00 |
| Total: | 100,00 |
Amount of time allocated to each course unit
| Designation | Time (hours) |
|---|---|
| Elaboração de projeto | 35,00 |
| Estudo autónomo | 88,00 |
| Frequência das aulas | 39,00 |
| Total: | 162,00 |
Eligibility for exams
Do not exceed the absence limit and submit all practical assignments.Calculation formula of final grade
The final assessment has two components:- theoretical part T = 0.7 average of the mini-tests
- practical part P = 0.3 min(average of practical work, T+3)
Final grade = T + P
Note: There is a supplementary exam for the theoretical part for those who did not pass.
Classification improvement
Performing the supplementary exam (to improve the T component).Page generated on: 2024-10-18 at 16:27:20 | Acceptable Use Policy | Data Protection Policy | Complaint Portal