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Institutionernas kurser för doktorander

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Kursplan för

Läsår
SSY280 - Model predictive control
 
Kursplanen fastställd 2012-02-23 av programansvarig (eller motsvarande)
Ägare: MPSYS
7,5 Poäng
Betygskala: TH - Fem, Fyra, Tre, Underkänt
Utbildningsnivå: Avancerad nivå
Huvudområde: Automation och mekatronik, Elektroteknik
Institution: 32 - ELEKTROTEKNIK


Undervisningsspråk: Engelska
Sökbar för utbytesstudenter
Blockschema: C

Modul   Poängfördelning   Tentamensdatum
Lp1 Lp2 Lp3 Lp4 Sommarkurs Ej Lp
0111 Konstruktionsövning + lab 4,5hp Betygskala: UG   4,5hp    
0211 Tentamen 3,0hp Betygskala: TH   3,0hp   12 Mar 2013 fm V,  15 Jan 2013 em M,  20 Aug 2013 fm V

I program

MPBME BIOMEDICAL ENGINEERING, MSC PROGR, Årskurs 2 (valbar)
MPSYS SYSTEMS, CONTROL AND MECHATRONICS, MSC PROGR, Årskurs 1 (obligatoriskt valbar)

Examinator:

Professor  Bo Egardt


Kursutvärdering:

http://document.chalmers.se/doc/6a0000da-4dc4-4094-91c3-fc13d1487a19


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Behörighet:

För kurser inom Chalmers utbildningsprogram gäller samma behörighetskrav som till de(t) program kursen ingår i.

Kursspecifika förkunskaper

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).

Syfte

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.

Lärandemål (efter fullgjord kurs ska studenten kunna)

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.

Innehåll

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.

Litteratur

To be decided.

Examination

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


Sidansvarig Publicerad: må 13 jul 2020.