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

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

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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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

Reference week

 MoTuWeThFr
8-9     
9-10     
10-11     
11-12     
12-13     
13-14    CE4
14-15    
15-16    CE4
16-17    
17-18     
18-19     
19-20     
20-21     
21-22     

Friday, 13h - 15h: Lecture CE4

Friday, 15h - 17h: Exercise, TP CE4