EL2700 Model Predictive Control 7.5 credits

Modell-prediktiv reglering

This is a course on model predictive control (MPC), or optimal control of systems with hard constraints on states and control inputs.   Contents; Properties of discrete-time linear systems in state-space form; optimal state transfer by linear and quadratic programming; design of linear-quadratic optimal controllers using dynamic programming; model predictive control and the receding horizon principle; dealing with state and control constraints; design and tuning of model predictive controllers and receding-horizon estimators; output feedback MPC; reference-following MPC; stability analysis of MPC controllers; implementation as explicit nonlinear feedback law or by real-time optimization.

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Course information

Content and learning outcomes

Course contents *

Properties of discrete-time linear systems in state-space form; optimal state transfer by linear and quadratic programming; design of linear-quadratic optimal controllers using dynamic programming; model predictive control and the receding horizon principle; dealing with state and control constraints; design and tuning of model predictive controllers and receding-horizon estimators; output feedback MPC; reference-following MPC; stability analysis of MPC controllers; implementation as explicit nonlinear feedback law or by real-time optimization

Intended learning outcomes *

After the course, you should be able to

  • analyze properties of discrete-time linear systems in state-space form
  • compute optimal open-loop controls for state transfer using linear and quadratic programming
  • use dynamic programming to design state estimators and linear controllers that minimize a quadratic cost criterion in the states and controls (LQG-optimal controllers)
  • understand the receding-horizon idea and how MPC extends LQG-optimal control to deal with state and control constraints
  • design MPC controllers for engeinering systems, making effective use of its tuning parameters to meet closed-loop performance targets
  • have a basic understanding of stability properties of MPC controllers
  • know how MPC can be implemented as either an nonlinear control law or using on-line optimization

Course Disposition

Lectures, Exercises, Computer exercises, Laboratory works. Homeworks

Literature and preparations

Specific prerequisites *

Automatic Control, Basic Course, or permission by the coordinator.

Recommended prerequisites

EL1000 Automatic Control Basic Course, or equivalent

Equipment

No information inserted

Literature

J. B. Rawlings and D. Q. Mayne, Model Predictive Control: Theory and Practice, Nob Hill Publishing, 2015.

Examination and completion

Grading scale *

A, B, C, D, E, FX, F

Examination *

  • LAB1 - Lab 1, 1.5 credits, Grading scale: P, F
  • LAB2 - Lab 2, 1.5 credits, Grading scale: P, F
  • LAB3 - Lab 3, 1.5 credits, Grading scale: P, F
  • TEN1 - Exam, 3.0 credits, Grading scale: A, B, C, D, E, FX, F

Based on recommendation from KTH’s coordinator for disabilities, the examiner will decide how to adapt an examination for students with documented disability.

The examiner may apply another examination format when re-examining individual students.

Other requirements for final grade *

LAB1 1.5p

LAB2 1.5p

LAB3 1.5p

TEN1 3p

Opportunity to complete the requirements via supplementary examination

No information inserted

Opportunity to raise an approved grade via renewed examination

No information inserted

Examiner

Mikael Johansson

Further information

Course web

Further information about the course can be found on the Course web at the link below. Information on the Course web will later be moved to this site.

Course web EL2700

Offered by

EECS/Intelligent Systems

Main field of study *

Electrical Engineering

Education cycle *

Second cycle

Add-on studies

No information inserted

Contact

Mikael Johansson (mikaelj@kth.se)

Ethical approach *

  • All members of a group are responsible for the group's work.
  • In any assessment, every student shall honestly disclose any help received and sources used.
  • In an oral assessment, every student shall be able to present and answer questions about the entire assignment and solution.

Supplementary information

In this course, the EECS code of honor applies, see:
http://www.kth.se/en/eecs/utbildning/hederskodex.