System identification using Kautz models
1994 (English)In: IEEE Transactions on Automatic Control, ISSN 0018-9286, E-ISSN 1558-2523, Vol. 39, no 6, 1276-1282 p.Article in journal (Refereed) Published
In this paper, the problem of approximating a linear time-invariant stable system by a finite weighted sum of given exponentials is considered. System identification schemes using Laguerre models are extended and generalized to Kautz models, which correspond to representations using several different possible complex exponentials. In particular, linear regression methods to estimate this sort of model from measured data are analyzed. The advantages of the proposed approach are the simplicity of the resulting identification scheme and the capability of modeling resonant systems using few parameters. The subsequent analysis is based on the result that the corresponding linear regression normal equations have a block Toeplitz structure. Several results on transfer function estimation are extended to discrete Kautz models, for example, asymptotic frequency domain variance expressions.
Place, publisher, year, edition, pages
1994. Vol. 39, no 6, 1276-1282 p.
Approximation theory, Discrete time control systems, Error statistics, Frequency domain analysis, Invariance, Linear control systems, Mathematical models, Parameter estimation, Regression analysis, Resonance, System stability, Transfer functions, Asymptotic variance expressions, Block Toeplitz structure, Complex exponentials, Discrete models, Kautz models, Laguerre models, Linear regression normal equations, Linear time invariant systems, Resonant systems, System identification, Identification (control systems)
IdentifiersURN: urn:nbn:se:kth:diva-55426DOI: 10.1109/9.293196ISI: A1994NU01800022OAI: oai:DiVA.org:kth-55426DiVA: diva2:471600
QC 20120104. Correspondence Address: Wahlberg, Bo; Royal Inst of Technology, Stockholm, Sweden2012-01-022012-01-022013-09-05Bibliographically approved