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A subspace fitting method for identification of linear state-space models
Department of Electrical and Computer Engineering, Brigham Young University, Provo, UT 84602 USA..
ArrayComm, Inc., Santa Clara, CA 95054 USA..
KTH, Superseded Departments, Signals, Sensors and Systems.ORCID iD: 0000-0003-2298-6774
Information Systems Lab, Stanford University, Stanford, CA 94305 USA..
1995 (English)In: IEEE Transactions on Automatic Control, ISSN 0018-9286, E-ISSN 1558-2523, Vol. 40, no 2, 311-316 p.Article in journal (Refereed) Published
Abstract [en]

A new method is presented for the identification of systems parameterized bylinear state-space models. The method relies on the concept of subspacefitting, wherein an input/output data model parameterized by the statematrices is found that best fits, in the least-squares sense, the dominantsubspace of the measured data. Some empirical results are included to illustrate the performance advantage of the algorithm compared to standard techniques

Place, publisher, year, edition, pages
IEEE Control Systems Society , 1995. Vol. 40, no 2, 311-316 p.
Keyword [en]
Hankel matrices, MIMO systems, identification, least squares approximations, linear systems, state-space methods, subspace fitting method
National Category
Signal Processing Control Engineering
URN: urn:nbn:se:kth:diva-53292DOI: 10.1109/9.341800ISI: A1995QF17100017OAI: diva2:469744
QC 20120103Available from: 2011-12-27 Created: 2011-12-27 Last updated: 2012-01-03Bibliographically approved

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