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 |
| carrona@ethz.ch | |
| Department | Mechanical and Process Engineering |
| Relationship | Lecturer |
| Number | Title | ECTS | Hours | Lecturers | |
|---|---|---|---|---|---|
| 151-0371-00L | Advanced Model Predictive Control Number of participants limited to 60. | 4 credits | 2V + 1U | M. Zeilinger, A. Carron, L. Hewing, J. Köhler | |
| 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. | ||||
| Learning 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 | ||||
| Lecture notes | Lecture notes will be provided. | ||||
| Prerequisites / Notice | Basic 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. | ||||
| 151-0575-01L | Signals and Systems  | 4 credits | 2V + 2U | A. Carron | |
| Abstract | Signals 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. | ||||
| Learning objective | Master the basics of signals and systems. Apply this knowledge to problems in the homework assignments and programming exercise. | ||||
| Content | Discrete-time signals and systems. Fourier- and z-Transforms. Frequency domain characterization of signals and systems. System identification. Time series analysis. Filter design. | ||||
| Lecture notes | Lecture notes available on course website. | ||||
| Prerequisites / Notice | Control Systems I is helpful but not required. | ||||