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.
| Original language | English |
|---|---|
| Pages (from-to) | 281-286 |
| Number of pages | 6 |
| Journal | IFAC Proceedings Volumes |
| Volume | 37 |
| Issue number | 9 |
| DOIs | |
| Publication status | Published - Jul 2004 |
| Externally published | Yes |
| Event | 7th IFAC Symposium on Dynamics and Control of Process Systems, DYCOPS 2004 - Cambridge, United States Duration: 5 Jul 2004 → 7 Jul 2004 |
Bibliographical note
Funding Information: This work is supported by Alexander von Humboldt Research Fellowship of Germany and this support is greatly acknowledged by BH and SXD. SJQ 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 identification
- Subspace PCA