Modelling and System Identification

Part 2 of the module Circuit Simulation and System Identification (or Fundamentals and Applications of System Identification) (ET-12 08 08) Mathematical models of physical and technical systems are an essential basis for virtual product development, the prediction of time series of process parameters and efficient engineering Algorithms ranging from active audio echo cancellation to state-based control of power systems. In this lecture, the basic methods of model building, especially from experimentally determined data, shall be imparted in order to be able to proceed purposefully in practical application problems.

Quick Overview

Subject contents
News/Current Information
Exam
Exercises
Literature

Lecturer

Dr.-Ing. Dirk Mayer

Subject contents

Categorization: Physical Modelling and Experimental System Analysis
Basic Identification Procedure, Classification of Identification Methods
Method of Least Squares for Static and Dynamic Systems
Non-Parametric Estimation: Spectral Estimation, Frequency Response Measurement, Correlation Analysis
State estimation with the Kalman filter
Outlook: Experimental Modeling

News/Current Information

OPAL site to course

Exam

There will be an oral exam at the end of the subject

Place:Fraunhofer IIS, Institutsteil Entwicklung Adaptiver Systeme EAS Münchner Straße 1601187 Dresden

Exercise tasks to download

Literature

[1] Isermann, Rolf: Identifikation dynamischer Systeme. Bd. 1: Frequenzgangmessung, Fourieranalyse, Korrelationsanalyse, Einführung in die Parameterschätzung Berlin [u.a.]: Springer, 1988. (Springer-Lehrbuch). ISBN 3-540-12635-X. - ISBN 0-387-12635-X

[2] Isermann, Rolf: Identifikation dynamischer Systeme. Bd. 2: Parameterschätzmethoden, Kennwertermittlung und Modellabgleich, Zeitvariante, nichtlineare und Mehrgrößen-Systeme, Anwendungen Berlin [u.a.]: Springer, 1988. (Springer-Lehrbuch). ISBN 3-540-18694-8. - ISBN 0-387-18694-8

[3] Ljung, Lennart: System Identification: Theory for the User 2nd ed. Upper Saddle River, N.J.: Prentice Hall PTR, 1999. (Prentice-Hall Information and System Sciences Series). ISBN 0-13-656695-2

[4] Nelles, Oliver. Nonlinear system identification: from classical approaches to neural networks and fuzzy models. Springer Science & Business Media, 2013.