Cascade structural model approximation of identified state space models
2008 (English)In: Proceedings of the IEEE Conference on Decision and Control, IEEE , 2008, 4198-4203 p.Conference paper (Refereed)
General black-box system identification techniques such as subspace system identification and FIR/ARX least squares system identification are commonly used to identify multi-input multi-output models from experimental data. However, in many applications there are a priori given structural information. Here the focus is on linear dynamical systems with a cascade structure, and with one input signal and two output signals. Models of such systems are important in e.g. cascade control applications. It is possible to incorporate such a structure in a prediction error method, which, however, is based on rather advanced numerical non-convex optimization techniques to calculate the corresponding structured model estimate. We will instead study how to use model approximation techniques to approximate a general black-box estimate with a structured model. This will avoid the use of numerical optimization and works well with e.g. subspace system identification, which is a standard method in process industry where cascade systems are very common. The problems of cascade structural model approximation and model reduction are rather non-standard, and we will study several new methods. The basic idea is to first find a higher order but structured model approximation using standard Hâ model matching techniques, and then in a second step use so-called structured balanced model reduction to find lower order structured approximation. Structured balanced model reduction is a rather new approach, with powerful model order selection tools and error bound results. The results of the corresponding two step model approximation approach seem promising, as illustrated by a simple numerical example.
Place, publisher, year, edition, pages
IEEE , 2008. 4198-4203 p.
, IEEE Conference on Decision and Control, ISSN 0191-2216
A-priori, Balanced model reductions, Basic ideas, Black boxes, Black-box systems, Cascade controls, Cascade structures, Cascade systems, Error bounds, Experimental datum, Higher orders, In process, Input signals, Least squares, Linear dynamical systems, Model reductions, Model-matching, Model-order selections, Multi input multi outputs, New approaches, Non-convex optimizations, Numerical examples, Numerical optimizations, Output signals, Prediction error methods, Standard h, Standard methods, State space models, Structural informations, Structural models, Structured models, Subspace system identifications, System identifications, TWo-step models, Use models, Cascade control systems, Cellular radio systems, Convex optimization, Curve fitting, Dynamical systems, Linear control systems, Mathematical models, Model structures, Standards, Identification (control systems)
IdentifiersURN: urn:nbn:se:kth:diva-28519DOI: 10.1109/CDC.2008.4739061ISI: 000307311604053ScopusID: 2-s2.0-62949212653ISBN: 978-142443124-3OAI: oai:DiVA.org:kth-28519DiVA: diva2:389927
47th IEEE Conference on Decision and Control, CDC 2008; Cancun; 9 December 2008 through 11 December 2008
FunderSwedish Research Council
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