Recursive subspace identification approach of a closed-loop model

Abstract

A subspace model identification algorithm under closed-loop experimental condition is presented in this paper that can be implemented to recursively identify and update system model. A new updating scheme is developed to obtain the projected data matrix recursively through sliding window technique and linear equation. Based on the propagator type method in array signal processing, the subspace spanned by the column vectors of the extended observability matrix is estimated without singular values decomposition. The proposed method is feasible for the closed-loop system contaminated with colored noises. The numerical example shows the effectiveness of the proposed algorithm.