Model predictive control
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.
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