|Name||Prof. Dr. Melanie Zeilinger|
|Field||Intelligent Control Systems|
Inst. Dynam. Syst. u. Regelungst.
ETH Zürich, LEE L 210
|Telephone||+41 44 632 53 45|
|Department||Mechanical and Process Engineering|
Does not take place this semester.
This course is part of a one-year course. The 14 credit points will be issued at the end of FS2023 with new enrolling for the same Focus Project in FS2023.
For MAVT BSc and ITET BSc only.
Prerequisites for the focus projects:
a. Basis examination successfully passed
b. Block 1 and 2 successfully passed
For enrollment, please contact the D-MAVT Student Administration.
|0 credits||15A||M. Zeilinger|
|Abstract||Students develop and build a product from A-Z! They work in teams and independently, learn to structure problems, to identify solutions, system analysis and simulations, as well as presentation and documentation techniques. They build the product with access to a machine shop and state of the art engineering tools (Matlab, Simulink, etc).|
|Objective||The various objectives of the Focus Project are:|
- Synthesizing and deepening the theoretical knowledge from the basic courses of the 1. - 4. semester
- Team organization, work in teams, increase of interpersonal skills
- Independence, initiative, independent learning of new topic contents
- Problem structuring, solution identification in indistinct problem definitions, searches of information
- System description and simulation
- Presentation methods, writing of a document
- Ability to make decisions, implementation skills
- Workshop and industrial contacts
- Learning and recess of special knowledge
- Control of most modern engineering tools (Matlab, Simulink, CAD, CAE, PDM)
|Prerequisites / Notice||This Focus-Project is supervised by the following lecturers:|
Siegwart, R., ASL
Haas, R., ASL
Beardsley P., Disney Research Zurich
|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.|
|Objective||Design, implement and analyze advanced MPC formulations for robust and stochastic uncertainty descriptions, in particular with data-driven formulations.|
- 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.
|364-1058-00L||Risk Center Seminar Series||0 credits||2S||H. Schernberg, D. Basin, A. Bommier, D. N. Bresch, S. Brusoni, L.‑E. Cederman, P. Cheridito, F. Corman, H. Gersbach, C. Hölscher, K. Paterson, G. Sansavini, B. Stojadinovic, B. Sudret, J. Teichmann, R. Wattenhofer, U. A. Weidmann, S. Wiemer, M. Zeilinger, R. Zenklusen|
|Abstract||This course is a mixture between a seminar primarily for PhD and postdoc students and a colloquium involving invited speakers. It consists of presentations and subsequent discussions in the area of modeling complex socio-economic systems and crises. Students and other guests are welcome.|
|Objective||Participants should learn to get an overview of the state of the art in the field, to present it in a well understandable way to an interdisciplinary scientific audience, to develop novel mathematical models for open problems, to analyze them with computers, and to defend their results in response to critical questions. In essence, participants should improve their scientific skills and learn to work scientifically on an internationally competitive level.|
|Content||This course is a mixture between a seminar primarily for PhD and postdoc students and a colloquium involving invited speakers. It consists of presentations and subsequent discussions in the area of modeling complex socio-economic systems and crises. For details of the program see the webpage of the colloquium. Students and other guests are welcome.|
|Lecture notes||There is no script, but a short protocol of the sessions will be sent to all participants who have participated in a particular session. Transparencies of the presentations may be put on the course webpage.|
|Literature||Literature will be provided by the speakers in their respective presentations.|
|Prerequisites / Notice||Participants should have relatively good mathematical skills and some experience of how scientific work is performed.|
|401-5860-00L||Seminar in Robotics for CSE||4 credits||2S||M. Hutter, R. Katzschmann, E. Konukoglu, B. Nelson, R. Siegwart, M. Zeilinger|
|Abstract||This course provides an opportunity to familiarize yourself with the advanced topics of robotics and mechatronics research. The study plan has to be discussed with the lecturer based on your specific interests and/or the relevant seminar series such as the IRIS's Robotics Seminars and BiRONZ lectures, for example.|
|Objective||The students are familiar with the challenges of the fascinating and interdisciplinary field of Robotics and Mechatronics. They are introduced in the basics of independent non-experimental scientific research and are able to summarize and to present the results efficiently.|
|Content||This 4 ECTS course requires each student to discuss a study plan with the lecturer and select minimum 10 relevant scientific publications to read through, or attend 5-10 lectures of the public robotics oriented seminars (e.g. Public robotics seminars such as the IRIS's Robotics Seminars http://www.iris.ethz.ch/iris/series/, and BiRONZ lectures http://www.birl.ethz.ch/bironz/index are good examples). At the end of semester, the results should be presented in an oral presentation and summarized in a report, which takes the discussion of the presentation into account.|