Raffaello D'Andrea: Catalogue data in Spring Semester 2023 |  |
| Name | Prof. Dr. Raffaello D'Andrea |
| Field | Dynamic Systems and Control |
| Address | Inst. Dynam. Syst. u. Regelungst. ETH Zürich, ML K 36.1 Sonneggstrasse 3 8092 Zürich SWITZERLAND |
| Telephone | +41 44 632 80 60 |
| Fax | +41 44 632 11 39 |
| rdandrea@ethz.ch | |
| Department | Mechanical and Process Engineering |
| Relationship | Full Professor |
| Number | Title | ECTS | Hours | Lecturers | |||||||||||||||||||||||||||||||||||||||||||||||
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| 151-0566-00L | Recursive Estimation  | 4 credits | 2V + 1U | R. D'Andrea | |||||||||||||||||||||||||||||||||||||||||||||||
| Abstract | Estimation of the state of a dynamic system based on a model and observations in a computationally efficient way. | ||||||||||||||||||||||||||||||||||||||||||||||||||
| Learning objective | Learn the basic recursive estimation methods and their underlying principles. | ||||||||||||||||||||||||||||||||||||||||||||||||||
| Content | Introduction to state estimation; probability review; Bayes' theorem; Bayesian tracking; extracting estimates from probability distributions; Kalman filter; extended Kalman filter; particle filter; observer-based control and the separation principle. | ||||||||||||||||||||||||||||||||||||||||||||||||||
| Lecture notes | Lecture notes available on course website: http://www.idsc.ethz.ch/education/lectures/recursive-estimation.html | ||||||||||||||||||||||||||||||||||||||||||||||||||
| Prerequisites / Notice | Requirements: Introductory probability theory and matrix-vector algebra. | ||||||||||||||||||||||||||||||||||||||||||||||||||
| Competencies |
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| 227-0920-00L | Seminar in Systems and Control Does not take place this semester. | 0 credits | 1S | F. Dörfler, R. D'Andrea, E. Frazzoli, M. H. Khammash, J. Lygeros, R. Smith, M. Zeilinger | |||||||||||||||||||||||||||||||||||||||||||||||
| Abstract | Current topics in Systems and Control presented mostly by external speakers from academia and industry. | ||||||||||||||||||||||||||||||||||||||||||||||||||
| Learning objective | Gain familiarity with the state of the art in control theory and practice. | ||||||||||||||||||||||||||||||||||||||||||||||||||
| Competencies |
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