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Syllabus for

Academic year
SSY281 - Model predictive control
Modellprediktiv reglering
 
Syllabus adopted 2018-02-13 by Head of Programme (or corresponding)
Owner: MPSYS
7,5 Credits
Grading: TH - Five, Four, Three, Fail
Education cycle: Second-cycle
Major subject: Automation and Mechatronics Engineering, Electrical Engineering
Department: 32 - ELECTRICAL ENGINEERING


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

Course module   Credit distribution   Examination dates
Sp1 Sp2 Sp3 Sp4 Summer course No Sp
0118 Design exercise 7,5c Grading: TH   7,5c    

In programs

MPEPO ELECTRIC POWER ENGINEERING, MSC PROGR, Year 1 (elective)
MPSYS SYSTEMS, CONTROL AND MECHATRONICS, MSC PROGR, Year 1 (compulsory elective)
MPBME BIOMEDICAL ENGINEERING, MSC PROGR, Year 2 (elective)

Examiner:

Paolo Falcone

Replaces

SSY280   Model predictive control


Eligibility:


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).

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 
2005. B. Egardt: Lecture Notes.

Examination including compulsory elements

Individual assignments with TH grading.


Published: Mon 28 Nov 2016.