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Graduate courses

Departments' graduate courses for PhD-students.


Syllabus for

Academic year
SSY280 - Model predictive control
Syllabus adopted 2015-02-17 by Head of Programme (or corresponding)
Owner: MPSYS
7,5 Credits
Grading: TH - Five, Four, Three, Not passed
Education cycle: Second-cycle
Major subject: Automation and Mechatronics Engineering, Electrical Engineering

Teaching language: English
Open for exchange students
Block schedule: D

Course module   Credit distribution   Examination dates
Sp1 Sp2 Sp3 Sp4 Summer course No Sp
0111 Design exercise + laboratory 4,5 c Grading: UG   4,5 c    
0211 Examination 3,0 c Grading: TH   3,0 c   18 Mar 2016 am M,  08 Apr 2016 pm M,  16 Aug 2016 am M

In programs



Professor  Bo Egardt


In order to be eligible for a second cycle course the applicant needs to fulfil the general and specific entry requirements of the programme that owns the course. (If the second cycle course is owned by a first cycle programme, second cycle entry requirements apply.)
Exemption from the eligibility requirement: Applicants enrolled in a programme at Chalmers where the course is included in the study programme are exempted from fulfilling these requirements.

Course specific prerequisites

A basic course in automatic control and familiarity with state space techniques and discrete time models (as taught in e.g. the MPSYS course Linear control system design).


The purpose of this course is to give an introduction to model predictive control (MPC), a control system design technique that has gained increased popularity in a number of 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.


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.


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


G. Goodwin, M.M. Seron, J.A. De Doná: Constrained Control and Estimation. Springer 2005.
B. Egardt: Lecture Notes.


Written exam with TH grading; assignments (pass/fail with bonus points).

Page manager Published: Thu 04 Feb 2021.