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

Author(s)

Jose A. Ramos, Nova Southeastern UniversityFollow
Paulo Lopes dos Santos
Jorge L. Martins de Carvalho

Document Type

Article

Publication Title

International Journal of Systems Science

ISSN

0020-7721

Publication Date

2008

Abstract

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

Volume

39

Issue

9

First Page

897

Last Page

911

NSUWorks Citation

Ramos, Jose A.; Lopes dos Santos, Paulo; and Martins de Carvalho, Jorge L., "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" (2008). CCE Faculty Articles. 430.
https://nsuworks.nova.edu/gscis_facarticles/430