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A Novel Subspace Identification Approach with Enforced Causal Models
University of Texas at Austin, USA.
University of Texas at Austin, USA.
Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
2005 (English)In: Automatica, ISSN 0005-1098, Vol. 41, no 12, 2043-2053 p.Article in journal (Refereed) Published
Abstract [en]

Subspace identification methods (SIMs) for estimating state-space models have been proven to be very useful and numerically efficient. They exist in several variants, but all have one feature in common: as a first step, a collection of high-order ARX models are estimated from vectorized input-output data. In order not to obtain biased estimates, this step must include future outputs. However, all but one of the submodels include non-causal input terms. The coefficients of them will be correctly estimated to zero as more data become available. They still include extra model parameters which give unnecessarily high variance, and also cause bias for closed-loop data. In this paper, a new model formulation is suggested that circumvents the problem. Within the framework, the system matrices (A,B,C,D) and Markov parameters can be estimated separately. It is demonstrated through analysis that the new methods generally give smaller variance in the estimate of the observability matrix and it is supported by simulation studies that this gives lower variance also of the system invariants such as the poles.

Place, publisher, year, edition, pages
Elsevier, 2005. Vol. 41, no 12, 2043-2053 p.
Keyword [en]
Causal model, Subspace identification, Variance analysis
National Category
Control Engineering
URN: urn:nbn:se:liu:diva-50352DOI: 10.1016/j.automatica.2005.06.010OAI: diva2:271248

© 2005 Elsevier Ltd. All rights reserved.

Available from: 2009-10-11 Created: 2009-10-11 Last updated: 2013-07-17

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