NDLI: Identification of nonlinear parameter-varying systems via canonical variate analysis

Content Provider	IEEE Xplore Digital Library
Author	Larimore, W.E.
Copyright Year	2013
Description	Author affiliation: Adaptics, Inc., McLean, VA, USA (Larimore, W.E.)
Abstract	This paper gives a tutorial presentation of the canonical variate analysis (CVA) method of subspace system identification for linear time- invariant (LTI), linear parameter-varying (LPV), and nonlinear parameter-varying (NLPV) systems. The paper takes a first principles statistical approach to rank determination of deterministic vectors usinig a singular value decomposition (SVD), followed by a statistical multivariate CVA as rank constrained regression. This is generalized to LTI dynamic systems, and extended directly to LPV and NLPV. The computational structure and problem size is very similar to LTI subspace methods except that the matrix row dimension (number of lagged variables of the past) is multiplied by the effective number of parameter-varying functions. This is in contrast with the exponential explosion in the number of variables using current subspace methods for LPV systems. Compared with current methods, initial results indicate much less computation, maximum likelihood accuracy, and better numerical stability. The method applies to systems with feedback, and can automatically remove a number of redundancies in the nonlinear models producing near minimal state orders and polynomial degrees by hypothesis testing. There is detailed discussion of the methods and structure of the computational modules.
Starting Page	2247
Ending Page	2262
File Size	232643
Page Count	16
File Format	PDF
ISBN	9781479901777
ISSN	07431619
e-ISBN	9781479901784
DOI	10.1109/ACC.2013.6580169
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-06-17
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	American Automatic Control Council(AACC)
Subject Keyword	Vectors Covariance matrices Noise Matrix decomposition Computational modeling Singular value decomposition
Content Type	Text
Resource Type	Article
Subject	Electrical and Electronic Engineering

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