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 language | English |
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Pages (from-to) | 53-66 |
Number of pages | 14 |
Journal | Journal of Process Control |
Volume | 15 |
Issue number | 1 |
Early online date | 15 Jun 2004 |
DOIs | |
Publication status | Published - Feb 2005 |
Externally published | Yes |
Keywords
- Closed-loop identification
- Instrument variable method
- PCA
- Projection
- Singular value decomposition
- Subspace PCA
- Subspace identification