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151-0371-00L 4 Credits DR , MSC D-ITET , D-MAVT , D-INFK , D-MATH

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Advanced Model Predictive Control

Lecturers & Examiners: Prof. Dr. Melanie Zeilinger, Dr. Andrea Carron, Dr. Lukas Hewing, Dr. Johannes Köhler

Number of participants limited to 60.

VVZ CR n/a

Last Updated: 2026-02-05 16:02:02

Abstract

Model predictive control (MPC) has established itself as a powerful control technique for complex systems under state and input constraints. This course discusses the theory and application of recent advanced MPC concepts, focusing on system uncertainties and safety, as well as data-driven formulations and learning-based control.

Objective

Design, implement and analyze advanced MPC formulations for robust and stochastic uncertainty descriptions, in particular with data-driven formulations.

Content

Topics include - Nominal MPC for uncertain systems (nominal robustness) - Robust MPC - Stochastic MPC - Review of regression methods - Set-membership Identification and robust data-driven MPC - Bayesian regression and stochastic data-driven MPC - MPC as safety filter for reinforcement learning

Resources

Lecture Notes

Lecture notes will be provided.

General Information

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

Examination

Type: session examination
Mode: oral 20 minutes

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	Advanced Model Predictive Control	Thu 08:15-10:00 (HG D 1.2)	2 h weekly
exercise	Advanced Model Predictive Control	Thu 10:15-11:00 (HG D 1.2)	1 h weekly

Offered In

Mechanical Engineering Master
- Core Courses
  - Robotics, Systems and Control (The courses listed in this category “Core Courses” are recommended. Alternative courses can be chosen in agreement with the tutor.)
Computational Science and Engineering Master
- Electives (In the ‘electives’ subcategory, at least two course units must be successfully completed.)
Electrical Engineering and Information Technology Master
- Master Studies (Programme Regulations 2018)
  - Electives (This is only a short selection. Other courses from the ETH course catalogue may be chosen in agreement with your tutor. As an alternative to the elective courses, students may do a second semester project or an internship in industry. Please consult your tutor.)
  - Systems and Control (The core courses and specialisation courses below are a selection for students who wish to specialise in the area of "Systems and Control", see . The individual study plan is subject to the tutor's approval.)
    - Core Courses (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 (Advanced core courses bring students to gain in-depth knowledge of the chosen specialization. They are MSc level only.)
Robotics, Systems and Control Master
- Core Courses
Doctorate Information Technology and Electrical Engineering (More Information at: )
- Subject Specialisation (A minimum of 12 ECTS credit points must be obtained during doctoral studies. The courses on offer below are only a small selection out of a much larger available number of courses. Please discuss your course selection with your PhD supervisor.)