Model predictive control
Caution, these contents corresponds to the coursebooks of last year
Summary
Provide an introduction to the theory and practice of Model Predictive Control (MPC). Main benefits of MPC: flexible specification of time-domain objectives, performance optimization of highly complex multivariable systems and ability to explicitly enforce constraints on system behavior.
Content
- Review of convex optimization and required optimal control theory.
- Receding-horizon control for constrained linear systems.
- Practical issues: Tracking and offset-free control of constrained systems.
- Theoretical properties of constrained control: Constraint satisfaction and invariant set theory, Stability of MPC.
- Introduction to advanced topics in predictive control.
- Simulation-based project providing practical experience with MPC.
Keywords
Multi-variable control, Constrained systems, Model-based Control, Optimization
Learning Prerequisites
Required courses
- Automatique or Control Systems
Recommended courses
- Multivariable systems or Dynamic coordination
Important concepts to start the course
- State-space modeling
- Basic concepts of stability
- Linear quadratic regulation
Learning Outcomes
By the end of the course, the student must be able to:
- Design an advanced controller for a dynamic system, A11
- Assess / Evaluate the stability, performance and robustness of a closed-loop system, A12
- Work out / Determine the performance (by simulations or experiments) of a mechatronic system, A21
- Assess / Evaluate Define (specifications) the control performance for mechatronic systems, A18
Transversal skills
- Write a scientific or technical report.
Teaching methods
Lectures, exercises and course project
Expected student activities
- Participate in lectures, exercises and course project
- Homework of about 2 hours per week
Assessment methods
- Reports on weekly exercises
- Report on simulation-based project
- Written final exam
Supervision
Office hours | No |
Assistants | Yes |
Forum | No |
Resources
Bibliography
All material can be downloaded from the moodle site.
Ressources en bibliothèque
- Predictive Control with Constraints / Maciejowski
- Model Predictive Control: Theory and Design / Rawlings
- Convex Optimization / Boyd
- Predictive Control for linear and hybrid systems / Borrelli
- Numerical Optimization / Nocedal
Websites
Moodle Link
In the programs
- Semester: Fall
- Exam form: Written (winter session)
- Subject examined: Model predictive control
- Lecture: 2 Hour(s) per week x 14 weeks
- Exercises: 2 Hour(s) per week x 14 weeks
- Type: optional
- Semester: Fall
- Exam form: Written (winter session)
- Subject examined: Model predictive control
- Lecture: 2 Hour(s) per week x 14 weeks
- Exercises: 2 Hour(s) per week x 14 weeks
- Type: optional
- Semester: Fall
- Exam form: Written (winter session)
- Subject examined: Model predictive control
- Lecture: 2 Hour(s) per week x 14 weeks
- Exercises: 2 Hour(s) per week x 14 weeks
- Type: optional
- Semester: Fall
- Exam form: Written (winter session)
- Subject examined: Model predictive control
- Lecture: 2 Hour(s) per week x 14 weeks
- Exercises: 2 Hour(s) per week x 14 weeks
- Type: optional
- Semester: Fall
- Exam form: Written (winter session)
- Subject examined: Model predictive control
- Lecture: 2 Hour(s) per week x 14 weeks
- Exercises: 2 Hour(s) per week x 14 weeks
- Type: optional
- Semester: Fall
- Exam form: Written (winter session)
- Subject examined: Model predictive control
- Lecture: 2 Hour(s) per week x 14 weeks
- Exercises: 2 Hour(s) per week x 14 weeks
- Type: optional
- Semester: Fall
- Exam form: Written (winter session)
- Subject examined: Model predictive control
- Lecture: 2 Hour(s) per week x 14 weeks
- Exercises: 2 Hour(s) per week x 14 weeks
- Type: optional
- Semester: Fall
- Exam form: Written (winter session)
- Subject examined: Model predictive control
- Lecture: 2 Hour(s) per week x 14 weeks
- Exercises: 2 Hour(s) per week x 14 weeks
- Type: optional
- Semester: Fall
- Exam form: Written (winter session)
- Subject examined: Model predictive control
- Lecture: 2 Hour(s) per week x 14 weeks
- Exercises: 2 Hour(s) per week x 14 weeks
- Type: mandatory
- Semester: Fall
- Exam form: Written (winter session)
- Subject examined: Model predictive control
- Lecture: 2 Hour(s) per week x 14 weeks
- Exercises: 2 Hour(s) per week x 14 weeks
- Type: mandatory
- Semester: Fall
- Exam form: Written (winter session)
- Subject examined: Model predictive control
- Lecture: 2 Hour(s) per week x 14 weeks
- Exercises: 2 Hour(s) per week x 14 weeks
- Type: optional
- Exam form: Written (winter session)
- Subject examined: Model predictive control
- Lecture: 2 Hour(s) per week x 14 weeks
- Exercises: 2 Hour(s) per week x 14 weeks
- Type: optional
- Semester: Fall
- Exam form: Written (winter session)
- Subject examined: Model predictive control
- Lecture: 2 Hour(s) per week x 14 weeks
- Exercises: 2 Hour(s) per week x 14 weeks
- Type: optional