Program Overview

---

Course Description

The course EL2700, Model Predictive Control, is a 7.5 credit course that focuses on the theory and application of model predictive control (MPC), or optimal control of systems with hard constraints on states and control inputs.

Course Contents

The course covers the following topics:

• 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

Information per Course Offering

The course is offered in the autumn semester, with the following details:

Information for Autumn 2026 Start

• Course location: KTH Campus
• Duration: 24 August 2026 - 23 October 2026
• Periods: Autumn 2026: P1 (7.5 hp)
• Pace of study: 50%
• Application code: 11069
• Form of study: Normal Daytime
• Language of instruction: English
• Number of places: 1 - 80
• Target group: Open for all programmes as long as it can be included in your programme

Part of Programme

The course is part of the following programmes:

• Master's Programme, Systems, Control and Robotics, year 2, RASM
• Master's Programme, Systems, Control and Robotics, year 1, RASM
• Master's Programme, Systems, Control and Robotics, year 2, LDCS, Mandatory
• Master's Programme, Electric Power Engineering, year 1
• Master's Programme, Systems, Control and Robotics, year 2
• Master's Programme, Systems, Control and Robotics, year 1
• Master's Programme, Vehicle Engineering, year 1
• Master's Programme, Vehicle Engineering, year 2

Contact

• Examiner: Mikael Johansson
• Course coordinator: Mikael Johansson
• Teachers: Mikael Johansson

Course Syllabus

The course syllabus is available as a PDF document.

Content and Learning Outcomes

Course Contents

The course gives a thorough treatment of theory and application of model predictive control. The following topics are treated:

• Analysis of properties of linear systems in discrete time
• Use of linear and quadratic programming to determine open loop control of linear systems in discrete time
• Use of dynamic programming to determine optimal observers and linear control systems that minimise quadratic objective functions in the control input and the system states (LQG control)
• The idea behind receding-horizon control and how model predictive control (MPC) expands on LQG to handle hard limitations on control inputs and system states
• Design of MPC controllers for technical systems and how different design parameters should be chosen to satisfy the performance requirements that are set on the closed system
• Stability properties of MPC controllers
• Implementation of MPC controllers either as an explicit non-linear control system (that is determined off-line) or through real time optimization in each sample

Intended Learning Outcomes

After passing the course, the student should be able to:

• Formulate theory and definitions of important concepts in model predictive control
• Apply theory and methods in model predictive control

Literature and Preparations

Specific Prerequisites

Automatic Control, general course or permission from the examiner

Recommended Prerequisites

EL1000 Automatic Control Basic Course, or equivalent

Literature

Information about course literature can be found in the course memo for the course offering or in the course room in Canvas.

Examination and Completion

Grading Scale

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

Examination

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

Other Requirements for Final Grade

• LAB1 1.5p Lab 1 Grading scale P/F
• LAB2 1.5p Lab 2 Grading scale P/F
• LAB3 1.5p Lab 3 Grading scale P/F
• TEN1 3p Written examination. Grading scale A-B-C-D-E-Fx-F

Further Information

Course Room in Canvas

Registered students can find further information about the implementation of the course in the course room in Canvas.

Offered by

EECS/Intelligent Systems

Main Field of Study

Electrical Engineering

Education Cycle

Second cycle

Supplementary Information

In this course, the EECS code of honor applies.