Closed-loop subspace identification : an orthogonal projection approach

Biao HUANG*, Steven X. DING, S. Joe QIN

*Corresponding author for this work

Research output: Journal PublicationsJournal Article (refereed)peer-review

166 Citations (Scopus)

Abstract

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. © 2004 Elsevier Ltd. All rights reserved.
Original languageEnglish
Pages (from-to)53-66
Number of pages14
JournalJournal of Process Control
Volume15
Issue number1
Early online date15 Jun 2004
DOIs
Publication statusPublished - Feb 2005
Externally publishedYes

Keywords

  • Closed-loop identification
  • Instrument variable method
  • PCA
  • Projection
  • Singular value decomposition
  • Subspace PCA
  • Subspace identification

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