Theory and techniques for the identification of dynamic models from experimentally obtained system input-output data.
Learning objective
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.
Content
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.
Literature
"System Identification; Theory for the User" Lennart Ljung, Prentice Hall (2nd Ed), 1999.
Additional papers will be available via the course Moodle.
Prerequisites / Notice
Control systems (227-0216-00L) or equivalent.
Performance assessment
Performance assessment information (valid until the course unit is held again)
Performance assessment as a semester course