Syllabus for |
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SSY281 - Model predictive control
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| Modellprediktiv reglering |
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| Syllabus adopted 2020-02-03 by Head of Programme (or corresponding) |
| Owner: MPSYS |
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7,5 Credits
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| Grading: TH - Pass with distinction (5), Pass with credit (4), Pass (3), Fail |
| Education cycle: Second-cycle |
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Major subject: Automation and Mechatronics Engineering, Electrical Engineering
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Department: 32 - ELECTRICAL ENGINEERING
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Teaching language: English
Application code: 35125
Open for exchange students: Yes
Block schedule:
D
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Sp1 |
Sp2 |
Sp3 |
Sp4 |
Summer course |
No Sp |
| 0118 |
Design exercise |
7,5 c |
Grading: TH |
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7,5 c
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09 Jun 2021 am J, |
17 Aug 2021 am J |
In programs
MPBME BIOMEDICAL ENGINEERING, MSC PROGR, Year 2 (elective)
MPEPO ELECTRIC POWER ENGINEERING, MSC PROGR, Year 1 (elective)
MPSYS SYSTEMS, CONTROL AND MECHATRONICS, MSC PROGR, Year 1 (compulsory elective)
Examiner:
Nikolce Murgovski
Go to Course Homepage
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
2005. B. Egardt: Lecture Notes.
Examination including compulsory elements
Individual assignments with TH grading.