Andrea Carron: Catalogue data in Autumn Semester 2022

Name Dr. Andrea Carron
Address
Intelligente Regelsysteme
ETH Zürich, LEE L 216
Leonhardstrasse 21
8092 Zürich
SWITZERLAND
Telephone+41 44 632 04 85
E-mailcarrona@ethz.ch
DepartmentMechanical and Process Engineering
RelationshipLecturer

NumberTitleECTSHoursLecturers
151-0371-00LAdvanced Model Predictive Control
Number of participants limited to 60.
4 credits2V + 1UM. Zeilinger, A. Carron, L. Hewing, J. Köhler
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.
151-0575-01LSignals and Systems Information 4 credits2V + 2UA. Carron
AbstractSignals arise in most engineering applications. They contain information about the behavior of physical systems. Systems respond to signals and produce other signals. In this course, we explore how signals can be represented and manipulated, and their effects on systems. We further explore how we can discover basic system properties by exciting a system with various types of signals.
ObjectiveMaster the basics of signals and systems. Apply this knowledge to problems in the homework assignments and programming exercise.
ContentDiscrete-time signals and systems. Fourier- and z-Transforms. Frequency domain characterization of signals and systems. System identification. Time series analysis. Filter design.
Lecture notesLecture notes available on course website.
Prerequisites / NoticeControl Systems I is helpful but not required.