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System Identification

R. S. Smith
P. Beuchat, G. Darivianakis, H. Hesse, T. A. Wood
Link zum Kurskatalog
Fall 2017
To provide a series of practical techniques for the development of dynamical models from experimental data, with the emphasis being on the development of models suitable for feedback control design purposes. To provide sufficient theory to enable the practitioner to understand the trade-offs between model accuracy, data quality and data quantity.
D-ITET Master, Systems and Control specialization
Recommended Core Courses

Control systems (227-0216-00L) or equivalent.

Familiarity with the following concepts is assumed:

  • Laplace and Fourier transforms;
  • Z-transform;
  • Differential and difference equations;
  • State-space representations;
  • Basic stochastic variable concepts.

Introduction to modeling: Black-box and grey-box models; Parametric and non-parametric models; ARX, ARMAX (etc.) models.
Predictive, open-loop, black-box identification methods. Time and frequency domain methods. Subspace identification methods.
Optimal experimental design, Cramer-Rao bounds, input signal design.
Parametric identification methods. On-line and batch approaches.
Closed-loop identification strategies. Trade-off between controller performance and information available for identification. Autotuners.
Model validation in classical and robust control frameworks. Set based modeling.
Iterative identification and design approaches.

"System Identification; Theory for the User" Lennart Ljung, Prentice Hall (2nd Ed), 1999. "Dynamic system identification: Experimental design and data analysis", GC Goodwin and RL Payne, Academic Press, 1977.