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
or (If possible)
Mayer, Dirk: Modelling and System Identification 6 DS., HSZ/0108/U; Monday
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
Exercises
Tutor: Dipl.-Ing. Fabian Paschke
Time: Thursday, odd weeks, 5th DS (double lesson or double period) (2:50 pm - 4:20 pm), Location: SCH/316a/U
Exercise tasks to download
Literature
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[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. |