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.

  • Education cycle

    Second cycle
  • Main field of study

    Electrical Engineering
  • Grading scale

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

Course offerings

Autumn 19 for programme students

Autumn 18 Doktorand for programme students

  • Periods

    Autumn 18 P1 (7.5 credits)

  • Application code

    51294

  • Start date

    27/08/2018

  • End date

    14/01/2019

  • Language of instruction

    English

  • Campus

    KTH Campus

  • Tutoring time

    Daytime

  • Form of study

    Normal

  • Number of places *

    1 - 1

    *) The Course date may be cancelled if number of admitted are less than minimum of places. If there are more applicants than number of places selection will be made.

  • Course responsible

    Mikael Johansson <mikaelj@kth.se>

  • Target group

    For doctoral students at KTH

Autumn 18 Doktorand for single courses students

  • Periods

    Autumn 18 P1 (7.5 credits)

  • Application code

    10132

  • Start date

    27/08/2018

  • End date

    26/10/2018

  • Language of instruction

    English

  • Campus

    KTH Campus

  • Tutoring time

    Daytime

  • Form of study

    Normal

  • Number of places *

    1 - 1

    *) The Course date may be cancelled if number of admitted are less than minimum of places. If there are more applicants than number of places selection will be made.

  • Course responsible

    Mikael Johansson <mikaelj@kth.se>

  • Target group

    For doctoral students at KTH

Autumn 18 Doktorand for single courses students

  • Periods

    Autumn 18 P1 (7.5 credits)

  • Application code

    10138

  • Start date

    27/08/2018

  • End date

    26/10/2018

  • Language of instruction

    English

  • Campus

    KTH Campus

  • Tutoring time

    Daytime

  • Form of study

    Normal

  • Number of places *

    1 - 1

    *) The Course date may be cancelled if number of admitted are less than minimum of places. If there are more applicants than number of places selection will be made.

  • Course responsible

    Mikael Johansson <mikaelj@kth.se>

  • Target group

    For doctoral students at KTH

Autumn 18 Doktorand for single courses students

  • Periods

    Autumn 18 P1 (7.5 credits)

  • Application code

    10144

  • Start date

    27/08/2018

  • End date

    26/10/2018

  • Language of instruction

    English

  • Campus

    KTH Campus

  • Tutoring time

    Daytime

  • Form of study

    Normal

  • Number of places *

    1 - 1

    *) The Course date may be cancelled if number of admitted are less than minimum of places. If there are more applicants than number of places selection will be made.

  • Course responsible

    Mikael Johansson <mikaelj@kth.se>

  • Target group

    For doctoral students at KTH

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 main content

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

Disposition

Lectures, Exercises, Computer exercises, Laboratory works. Homeworks

Eligibility

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

Recommended prerequisites

EL1000 Automatic Control Basic Course, or equivalent

Literature

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

Examination

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

Requirements for final grade

LAB1 1.5p

LAB2 1.5p

LAB3 1.5p

TEN1 3p

Offered by

EECS/Intelligent Systems

Contact

Mikael Johansson (mikaelj@kth.se)

Examiner

Mikael Johansson <mikaelj@kth.se>

Version

Course syllabus valid from: Spring 2019.
Examination information valid from: Spring 2019.