Suchergebnis: Katalogdaten im Frühjahrssemester 2023
Mathematik Master  | |||||||||||||||||||||||||||||||||||||||||||||||||||
 AnwendungsgebietNur für das Master-Diplom in Angewandter Mathematik erforderlich und anrechenbar. In der Kategorie Anwendungsgebiet für den Master in Angewandter Mathematik muss eines der zur Auswahl stehenden Anwendungsgebiete gewählt werden. Im gewählten Anwendungsgebiet müssen mindestens 8 KP erworben werden. Kreditpunkte aus anderen Anwendungsgebieten sind nicht für weitere Anwendungsgebiete anrechenbar. | |||||||||||||||||||||||||||||||||||||||||||||||||||
| Control and Automation | |||||||||||||||||||||||||||||||||||||||||||||||||||
| Nummer | Titel | Typ | ECTS | Umfang | Dozierende | ||||||||||||||||||||||||||||||||||||||||||||||
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| 151-0660-00L | Model Predictive Control | W | 4 KP | 2V + 1U | M. Zeilinger | ||||||||||||||||||||||||||||||||||||||||||||||
| Kurzbeschreibung | Model predictive control is a flexible paradigm that defines the control law as an optimization problem, enabling the specification of time-domain objectives, high performance control of complex multivariable systems and the ability to explicitly enforce constraints on system behavior. This course provides an introduction to the theory and practice of MPC and covers advanced topics. | ||||||||||||||||||||||||||||||||||||||||||||||||||
| Lernziel | Design and implement Model Predictive Controllers (MPC) for various system classes to provide high performance controllers with desired properties (stability, tracking, robustness,..) for constrained systems. | ||||||||||||||||||||||||||||||||||||||||||||||||||
| Inhalt | - Review of required optimal control theory - Basics on optimization - Receding-horizon control (MPC) for constrained linear systems - Theoretical properties of MPC: Constraint satisfaction and stability - Computation: Explicit and online MPC - Practical issues: Tracking and offset-free control of constrained systems, soft constraints - Robust MPC: Robust constraint satisfaction - Simulation-based project providing practical experience with MPC | ||||||||||||||||||||||||||||||||||||||||||||||||||
| Skript | Script / lecture notes will be provided. | ||||||||||||||||||||||||||||||||||||||||||||||||||
| Voraussetzungen / Besonderes | One semester course on automatic control, Matlab, linear algebra. Courses on signals and systems and system modeling are recommended. Important concepts to start the course: State-space modeling, basic concepts of stability, linear quadratic regulation / unconstrained optimal control. Expected student activities: Participation in lectures, exercises and course project; homework (~2hrs/week). | ||||||||||||||||||||||||||||||||||||||||||||||||||
| 227-0207-00L | Nonlinear Systems and Control Voraussetzung: Control Systems (227-0103-00L) | W | 6 KP | 4G | E. Gallestey Alvarez, P. F. Al Hokayem | ||||||||||||||||||||||||||||||||||||||||||||||
| Kurzbeschreibung | Introduction to the area of nonlinear systems and their control. Familiarization with tools for analysis of nonlinear systems. Discussion of the various nonlinear controller design methods and their applicability to real life problems. | ||||||||||||||||||||||||||||||||||||||||||||||||||
| Lernziel | On completion of the course, students understand the difference between linear and nonlinear systems, know the mathematical techniques for analysing these systems, and have learnt various methods for designing controllers accounting for their characteristics. Course puts the student in the position to deploy nonlinear control techniques in real applications. Theory and exercises are combined for better understanding of the virtues and drawbacks present in the different methods. | ||||||||||||||||||||||||||||||||||||||||||||||||||
| Inhalt | Virtually all practical control problems are of nonlinear nature. In some cases application of linear control methods leads to satisfactory controller performance. In many other cases however, only application of nonlinear analysis and control synthesis methods will guarantee achievement of the desired objectives. During the past decades mature nonlinear controller design methods have been developed and have proven themselves in applications. After an introduction of the basic methods for analysing nonlinear systems, these methods will be introduced together with a critical discussion of their pros and cons. Along the course the students will be familiarized with the basic concepts of nonlinear control theory. This course is designed as an introduction to the nonlinear control field and thus no prior knowledge of this area is required. The course builds, however, on a good knowledge of the basic concepts of linear control and mathematical analysis. | ||||||||||||||||||||||||||||||||||||||||||||||||||
| Skript | An english manuscript will be made available on the course homepage during the course. | ||||||||||||||||||||||||||||||||||||||||||||||||||
| Literatur | H.K. Khalil: Nonlinear Systems, Prentice Hall, 2001. | ||||||||||||||||||||||||||||||||||||||||||||||||||
| Voraussetzungen / Besonderes | Prerequisites: Linear Control Systems, or equivalent. | ||||||||||||||||||||||||||||||||||||||||||||||||||
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| 151-0530-00L | Nonlinear Dynamics and Chaos II | W | 4 KP | 4G | G. Haller | ||||||||||||||||||||||||||||||||||||||||||||||
| Kurzbeschreibung | The internal structure of chaos; Hamiltonian dynamical systems; Normally hyperbolic invariant manifolds; Geometric singular perturbation theory; Finite-time dynamical systems | ||||||||||||||||||||||||||||||||||||||||||||||||||
| Lernziel | The course introduces the student to advanced, comtemporary concepts of nonlinear dynamical systems analysis. | ||||||||||||||||||||||||||||||||||||||||||||||||||
| Inhalt | I. The internal structure of chaos: symbolic dynamics, Bernoulli shift map, sub-shifts of finite type; chaos is numerical iterations. II.Hamiltonian dynamical systems: conservation and recurrence, stability of fixed points, integrable systems, invariant tori, Liouville-Arnold-Jost Theorem, KAM theory. III. Normally hyperbolic invariant manifolds: Crash course on differentiable manifolds, existence, persistence, and smoothness, applications. IV. Geometric singular perturbation theory: slow manifolds and their stability, physical examples. V. Finite-time dynamical system; detecting Invariant manifolds and coherent structures in finite-time flows | ||||||||||||||||||||||||||||||||||||||||||||||||||
| Skript | Handwritten instructor's notes and typed lecture notes will be downloadable from Moodle. | ||||||||||||||||||||||||||||||||||||||||||||||||||
| Literatur | Books will be recommended in class | ||||||||||||||||||||||||||||||||||||||||||||||||||
| Voraussetzungen / Besonderes | Nonlinear Dynamics I (151-0532-00) or equivalent | ||||||||||||||||||||||||||||||||||||||||||||||||||
| 151-0566-00L | Recursive Estimation | W | 4 KP | 2V + 1U | R. D'Andrea | ||||||||||||||||||||||||||||||||||||||||||||||
| Kurzbeschreibung | Estimation of the state of a dynamic system based on a model and observations in a computationally efficient way. | ||||||||||||||||||||||||||||||||||||||||||||||||||
| Lernziel | Learn the basic recursive estimation methods and their underlying principles. | ||||||||||||||||||||||||||||||||||||||||||||||||||
| Inhalt | 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. | ||||||||||||||||||||||||||||||||||||||||||||||||||
| Skript | Lecture notes available on course website: http://www.idsc.ethz.ch/education/lectures/recursive-estimation.html | ||||||||||||||||||||||||||||||||||||||||||||||||||
| Voraussetzungen / Besonderes | Requirements: Introductory probability theory and matrix-vector algebra. | ||||||||||||||||||||||||||||||||||||||||||||||||||
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