CEC Faculty Articles

Title

Subspace Identification of Linear Parameter-Varying Systems with Innovation-Type Noise Models Driven by General Inputs and a Measurable White Noise Time-Varying Parameter Vector

Document Type

Article

Date

2008

Publication Title

International Journal of Systems Science

ISSN or ISBN

0020-7721

Volume

39

Issue

9

First Page

897

Last Page

911

Description

In this article, we introduce an iterative subspace system identification algorithm for MIMO linear parameter-varying systems with innovation-type noise models driven by general inputs and a measurable white noise time-varying parameter vector. The new algorithm is based on a convergent sequence of linear deterministic–stochastic state-space approximations, thus considered a Picard-based method. Such methods have proven to be convergent for the bilinear state-space system identification problem. Their greatest strength lies on the dimensions of the data matrices that are comparable to those of a linear subspace algorithm, thus avoiding the curse of dimensionality.

DOI

10.1080/00207720802184741

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