151-0371-00L  Advanced Model Predictive Control

SemesterAutumn Semester 2022
LecturersM. Zeilinger, A. Carron, L. Hewing, J. Köhler
Periodicityyearly recurring course
Language of instructionEnglish
CommentNumber of participants limited to 60.

AbstractModel 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.
ObjectiveDesign, implement and analyze advanced MPC formulations for robust and stochastic uncertainty descriptions, in particular with data-driven formulations.
ContentTopics 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
Lecture notesLecture notes will be provided.
Prerequisites / NoticeBasic courses in control, advanced course in optimal control, basic MPC course (e.g. 151-0660-00L Model Predictive Control) strongly recommended.
Background in linear algebra and stochastic systems recommended.