Model predictive control

ME-425 / 4 credits

Teacher: Jones Colin Neil

Language: English

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
  • Courses: 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
  • Courses: 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
  • Courses: 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
  • Courses: 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
  • Courses: 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
  • Courses: 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
  • Courses: 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
  • Courses: 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
  • Courses: 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
  • Courses: 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
  • Courses: 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
  • Courses: 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
  • Courses: 2 Hour(s) per week x 14 weeks
  • Exercises: 2 Hour(s) per week x 14 weeks
  • Type: optional

Reference week

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