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 |
|---|---|
| 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 |
Funding
This work is supported by Alexander von Humboldt Research Fellowship of Germany and this support is greatly acknowledged by B.H. and S.X.D. S.J.Q. also acknowledges the support from Natural Sciences Foundation of China in the form of an Outstanding Young Investigator Award for Overseas (60228001).
Keywords
- Closed-loop identification
- Instrument variable method
- PCA
- Projection
- Singular value decomposition
- Subspace PCA
- Subspace identification