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N2SID: Nuclear norm subspace identification of innovation models
Delft University of Technology, Netherlands.
Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, Faculty of Science & Engineering.
2016 (English)In: Automatica, ISSN 0005-1098, E-ISSN 1873-2836, Vol. 72, 57-63 p.Article in journal (Refereed) Published
Abstract [en]

The identification of multivariable state space models in innovation form is solved in a subspace identification framework using convex nuclear norm optimization. The convex optimization approach allows to include constraints on the unknown matrices in the data-equation characterizing subspace identification methods, such as the lower triangular block-Toeplitz of weighting matrices constructed from the Markov parameters of the unknown observer. The classical use of instrumental variables to remove the influence of the innovation term on the data equation in subspace identification is avoided. The avoidance of the instrumental variable projection step has the potential to improve the accuracy of the estimated model predictions, especially for short data length sequences. (C) 2016 Elsevier Ltd. All rights reserved.

Place, publisher, year, edition, pages
PERGAMON-ELSEVIER SCIENCE LTD , 2016. Vol. 72, 57-63 p.
Keyword [en]
Subspace system identification; Optimization; Structural constraints; Innovation state space models
National Category
Control Engineering
Identifiers
URN: urn:nbn:se:liu:diva-132049DOI: 10.1016/j.automatica.2016.05.021ISI: 000383818800008OAI: oai:DiVA.org:liu-132049DiVA: diva2:1038471
Note

Funding Agencies|European Research Council [339681]

Available from: 2016-10-18 Created: 2016-10-17 Last updated: 2016-10-18

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