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.



Courses

NumberTitleHoursLecturers
151-0371-00 VAdvanced Model Predictive Control2 hrs
Thu08:15-10:00HG D 1.2 »
M. Zeilinger, A. Carron, L. Hewing, J. Köhler
151-0371-00 UAdvanced Model Predictive Control1 hrs
Thu10:15-11:00HG D 1.2 »
M. Zeilinger, A. Carron, L. Hewing, J. Köhler

Catalogue data

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.

Performance assessment

Performance assessment information (valid until the course unit is held again)
Performance assessment as a semester course
ECTS credits4 credits
ExaminersM. Zeilinger, A. Carron, L. Hewing, J. Köhler
Typesession examination
Language of examinationEnglish
RepetitionThe performance assessment is offered every session. Repetition possible without re-enrolling for the course unit.
Mode of examinationoral 20 minutes
Additional information on mode of examinationThe 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.
This information can be updated until the beginning of the semester; information on the examination timetable is binding.

Learning materials

No public learning materials available.
Only public learning materials are listed.

Groups

No information on groups available.

Restrictions

There are no additional restrictions for the registration.

Offered in

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