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151-0660-00L 4 Credits BSC , DR , MSC D-ERDW , D-ARCH , D-MAVT , D-INFK , D-MATH , D-PHYS , D-ITET

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Model Predictive Control

Lecturers & Examiners: Prof. Dr. Melanie Zeilinger

VVZ CR 3.4

Last Updated: 2026-06-01 11:33:15

Abstract

Model predictive control is a flexible paradigm that defines the control law as an optimization problem, enabling the specification of time-domain objectives, high performance control of complex multivariable systems and the ability to explicitly enforce constraints on system behavior. This course provides an introduction to the theory and practice of MPC and covers advanced topics.

Objective

Design and implement Model Predictive Controllers (MPC) for various system classes to provide high performance controllers with desired properties (stability, tracking, robustness,..) for constrained systems.

Content

- Review of required optimal control theory - Basics on optimization - Receding-horizon control (MPC) for constrained linear systems - Theoretical properties of MPC: Constraint satisfaction and stability - Computation: Explicit and online MPC - Practical issues: Tracking and offset-free control of constrained systems, soft constraints - Robust MPC: Robust constraint satisfaction - Simulation-based project providing practical experience with MPC

Resources

Lecture Notes

Script / lecture notes will be provided.

Learning Materials (Links)

Main link
Course webpage

General Information

Language: English
Levels: BSC , DR , MSC
Frequency: Yearly recurring

Examination

Type: session examination
Mode: written 120 minutes
Aids: Four A4 pages (2 A4 sheets double-sided or 4 A4 sheets single-sided, handwritten or computer typed)

The final grade is based on an exam and an optional take-home project. The exam takes place during the examination session. The project is a continuous performance assessment (learning task) and requires the student to understand and apply the lecture material.The grade of the project may contribute 0.25 grade points to the final grade, but only if it helps improving the final grade.

Course Components

Type	Title	Time & Place	Hours
lecture	Model Predictive Control	Thu 10:15-12:00 (ML D 28)	2 h weekly
exercise	Model Predictive Control	Thu 12:15-13:00 (ML D 28)	1 h weekly

Offered In

Rechnergestützte Wissenschaften Bachelor
- Weitere Wahlfächer aus den Vertiefungsgebieten (RW Master) (227-0662-00L und 227-0662-10L sind nur zusammen anrechenbar)
Maschineningenieurwissenschaften Master
- Kernfächer
Rechnergestützte Wissenschaften Master
- Vertiefungsgebiete
  - Systems and Control
Mathematik Master
- Anwendungsgebiet (Nur für das Master-Diplom in Angewandter Mathematik erforderlich und anrechenbar. In der Kategorie Anwendungsgebiet für den Master in Angewandter Mathematik muss eines der zur Auswahl stehenden Anwendungsgebiete gewählt werden. Im gewählten Anwendungsgebiet müssen mindestens 8 KP erworben werden. Kreditpunkte aus anderen Anwendungsgebieten sind nicht für weitere Anwendungsgebiete anrechenbar.)
  - Control and Automation
Elektrotechnik und Informationstechnologie Master
- Master-Studium (Studienreglement 2018)
  - Vertiefung: Systems and Control (The core courses and specialization courses below are a selection for students who wish to specialize in the area of "Systems and Control", see . The individual study plan is subject to the tutor's approval.)
    - Kernfächer (These core courses are particularly recommended for the field of "Systems and Control". You may choose core courses form other fields in agreement with your tutor. A minimum of 24 credits must be obtained from core courses during the MSc EEIT.)
      - Advanced Core Courses
- Master-Studium (Studienreglement 2008)
  - Fächer der Vertiefung (A total of 42 CP must be achieved form courses during the Master Program. The individual study plan is subject to the tutor's approval. Semester / Research Projects are not allowed in this category.)
    - Energy and Power Electronics
      - Empfohlene Fächer (Diese Fächer sind eine Empfehlung. Sie können Fächer aus allen Vertiefungsrichtungen wählen. Sprechen Sie mit Ihrem Tutor.)
    - Systems and Control
      - Kernfächer (Diese Fächer sind besonders Empfohlen, um sich in "Systems and Control" zu vertiefen.)
Robotics, Systems and Control Master
- Kernfächer
Doktorat Informationstechnologie und Elektrotechnik (A minimum of 12 ECTS credit points must be obtained during doctoral studies (also see sub-categories for details) More Information at )
- Vertiefung Fachwissen (The courses on offer below are but a small selection out of a much larger available number of courses. Please discuss your course selection with your PhD supervisor.)
Integrated Building Systems Master
- Hauptfächer
  - Vertiefungsfächer
Data Science Master
- Master-Studium (Studienreglement 2023)
  - Wahlfächer
    - Interdisziplinäre Wahlfächer
Space Systems Master
- Fachspezifische Vertiefung (Es müssen mindestens 20 KP aus den Deep Track Lerneinheiten absolviert werden. Überzählige KP können für Wahlfächer angerechnet werden.)
  - Robotics
    - Vertiefungsfächer Robotics (Diese LE's können sowohl als Vertiefungsfach als auch als Wahlfach angerechnet werden.)