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151-0371-00L 4 Credits MSC D-ITET , D-MAVT , D-INFK
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Advanced Model Predictive Control

Lecturers: Prof. Dr. Melanie Zeilinger, Dr. Andrea Carron, Dr. Lukas Hewing
Examiners: Prof. Dr. Melanie Zeilinger, Dr. Lukas Hewing
VVZ CR n/a

Last Updated: 2026-02-05 15:35:19

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 - Review of Bayesian statistics, stochastic systems and Stochastic Optimal Control - Nominal MPC for uncertain systems (nominal robustness) - Robust MPC - Stochastic MPC - 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
MSC
Frequency
Yearly recurring

Examination

Type
session examination
Mode
oral 20 minutes

Registration & Places

Max Places
40

Course Components

Type Title Time & Place Hours
lecture Advanced Model Predictive Control
This course will be taught in a hybrid of online and face-to-face classroom formats; students will be informed about who can attend the class on campus or join an online class.
  • Thu 10:00-12:00 (ON LI NE)
2 h weekly
exercise Advanced Model Predictive Control
This course will be taught in a hybrid of online and face-to-face classroom formats; students will be informed about who can attend the class on campus or join an online class.
  • Thu 12:00-13:00 (ON LI NE)
1 h weekly

Offered In