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Syllabus for

Academic year
SSY280 - Model predictive control
 
Syllabus adopted 2013-02-19 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
Department: 32 - ELECTRICAL ENGINEERING


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

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   13 Mar 2014 am H,  17 Jan 2014 pm M,  19 Aug 2014 am M

In programs

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

Examiner:

Professor  Bo Egardt


Course evaluation:

http://document.chalmers.se/doc/0bd1eb07-9867-435a-81d8-4824f15d89d8


  Go to Course Homepage

Eligibility:

For single subject courses within Chalmers programmes the same eligibility requirements apply, as to the programme(s) that the course is part of.

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 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 and application to lab-scale processes.

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 a mandatory design and laboratory module, including assignments and laboratory experiments.

Literature

J. Maciejowski: Predictive Control with Constraints. Prentice Hall 2002.
B. Egardt: Lecture Notes.

Examination

Written exam with TH grading; design and laboratory module (pass/fail).


Page manager Published: Mon 28 Nov 2016.