Identification of a weighted combination of multivariable local linear state-space systems from input and output data

Author

Verdult, Vincent ; Verhaegen, Michel

Author_Institution

Fac. of Appl. Phys., Twente Univ., Enschede, Netherlands

Volume

5

fYear

2001

fDate

2001

Firstpage

4760

Abstract

Discusses a method for the determination of a weighted combination of local linear state-space systems from input and output data. The method is iterative and each iteration consists of two steps. The first step is to determine the weighting functions given the local models. This problem is solved by using an extended Kalman smoother. The second step is to identify the local models given the weights. For this step we optimize a cost function that represents a tradeoff between local and global learning. For this optimization we use a gradient search method in combination with an appropriate projection in the parameter space to deal with similarity transformations

Keywords

Jacobian matrices; Kalman filters; covariance matrices; iterative methods; linear systems; multivariable systems; nonlinear filters; parameter estimation; smoothing methods; state estimation; state-space methods; cost function; extended Kalman smoother; global learning; gradient search method; identification; input data; local learning; local models; multivariable local linear state-space systems; output data; similarity transformations; weighted combination; Convergence; Cost function; Equations; Iterative methods; Kalman filters; Nonlinear systems; Optimization methods; Physics; Search methods; Tellurium;

fLanguage

English

Publisher

ieee

Conference_Titel

Decision and Control, 2001. Proceedings of the 40th IEEE Conference on

Conference_Location

Orlando, FL

Print_ISBN

0-7803-7061-9

Type

conf

DOI

10.1109/.2001.980959

Filename

980959