A New Subspace Identification Approach Based on Principal Component Analysis and Noise Estimation

Abstract

In this paper, a new subspace identification approach based on principal component analysis (PCA) and noise estimation is developed for multivariable dynamic process modeling. In contrast to typical subspace identification methods based on standard PCA with instrumental variables, the noise term is first estimated and naturally eliminated in the proposed approach, and then a PCA procedure is used to determine system observability subspace and extract system matrices A, B, C, and D from the estimated observability subspace. For comparison with other typical subspace identification methods based on PCA, numerical simulation and activated sludge process benchmark modeling are included to demonstrate the superiority of the proposed approach and reveal the probable reason for unsatisfied B and D estimations derived by some subspace identification methods based on PCA.