Improved closed-loop subspace identification based on principal component analysis and prior information

Abstract

Subspace identification is a very useful tool for estimating a state-space model for a dynamic system. However, most of the subspace identification methods (SIMs) can only provide consistent estimations when the quality of the data is good. This problem can be solved by integrating prior information about a system into an identification procedure. In this paper, we propose a new approach for closed-loop SIMs based on principal component analysis (PCA) utilizing prior information. After performing the PCA procedure, we use the constrained least squares (CLS) approach with an equality constraint to incorporate prior information into the impulse response. The simulation results reveal that the proposed methods are more accurate and stable in model identification.