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.
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