Course syllabus for Model predictive control

Course syllabus adopted 2021-02-08 by Head of Programme (or corresponding).

Overview

  • Swedish nameModellprediktiv reglering
  • CodeSSY281
  • Credits7.5 Credits
  • OwnerMPSYS
  • Education cycleSecond-cycle
  • Main field of studyAutomation and Mechatronics Engineering, Electrical Engineering
  • DepartmentELECTRICAL ENGINEERING
  • GradingTH - Pass with distinction (5), Pass with credit (4), Pass (3), Fail

Course round 1

  • Teaching language English
  • Application code 35125
  • Block schedule
  • Open for exchange studentsYes

Credit distribution

0118 Design exercise 7.5 c
Grading: TH		 7.5 c

In programmes

Examiner

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Eligibility

General entry requirements for Master's level (second cycle)
Applicants enrolled in a programme at Chalmers where the course is included in the study programme are exempted from fulfilling the requirements above.

Specific entry requirements

English 6 (or by other approved means with the equivalent proficiency level)
Applicants enrolled in a programme at Chalmers where the course is included in the study programme are exempted from fulfilling the requirements above.

Course specific prerequisites

Familiarity with state space techniques, including stability, state feedback, and discrete time models, is required. The course Linear control system design (SSY285) contains the relevant material.

Aim

The purpose of this course is to introduce model predictive control (MPC), a control system design technique that has gained increased popularity in several application areas during recent years. Important reasons for this are the ability to treat multi-input, multi-output systems in a systematic way, and the possibility to include, in a very explicit way, constraints on states and control inputs in the design. The intention with the course is to cover the mathematical foundations as well as implementation issues, and to give hands-on experience from computer simulations.

Learning outcomes (after completion of the course the student should be able to)

  • Understand and explain the basic principles of model predictive control, its pros and cons, and the challenges met in implementation and applications.
  • Correctly state, in mathematical form, MPC formulations based on descriptions of control problems expressed in application terms.
  • Describe and construct MPC controllers based on a linear model, quadratic costs and linear constraints.
  • Describe basic properties of MPC controllers and analyze algorithmic details on very simple examples.
  • Understand and explain basic properties of the optimization problem as an ingredient of MPC, in particular concepts like linear, quadratic and convex optimization, optimality conditions, and feasibility.
  • Use software tools for analysis and synthesis of MPC controllers.

Content

  • Review of linear state space models and unconstrained linear quadratic control.
  • Fundamental concepts in constrained optimization, linear and quadratic programming, convexity.
  • Unconstrained and constrained optimal control. Receding horizon control, MPC controllers, review and classification.
  • Properties of MPC. Stability and feasibility.
  • Implementation issues.
  • Applications: examples and practical issues.

Organisation

The course comprises a number of lectures, problem sessions, and mandatory individual assignments.

Literature

James B. Rawlings,‎ David Q. Mayne,‎ Moritz M. Diehl. Model Predictive Control: Theory, Computation and Design. Nob Hill 2017, 2nd edition. 
Lecture notes.

Examination including compulsory elements

Individual assignments with TH grading.

The course examiner may assess individual students in other ways than what is stated above if there are special reasons for doing so, for example if a student has a decision from Chalmers on educational support due to disability.