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
International Journal of Systems Science
ISSN or ISBN
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.
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.