期刊
JOURNAL OF PROCESS CONTROL
卷 15, 期 1, 页码 53-66出版社
ELSEVIER SCI LTD
DOI: 10.1016/j.jprocont.2004.04.007
关键词
subspace identification; closed-loop identification; projection; instrument variable method; PCA; subspace PCA; singular value decomposition
In this paper, a closed-loop subspace identification approach through an orthogonal projection and subsequent singular value decomposition is proposed. As a by-product of this development, it explains why some existing subspace methods may deliver a bias in the presence of the feedback control and suggests a remedy to eliminate the bias. Furthermore, as the proposed method is a projection based method, it can simultaneously provide extended observability matrix, lower triangular block-Toeplitz matrix, and Kalman filtered state sequences. Therefore, using this method, the system state space matrices can be recovered either from the extended observability matrix/the block-Toeplitz matrix or from the Kalman filter state sequences. (C) 2004 Elsevier Ltd. All rights reserved.
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