A novel recursive subspace identification approach of closed-loop systems

Mathematical and Computer Modelling of Dynamical Systems

Volumn 19, Issue 6, 2013, Pages 526-539

  Wang, Jia ^a   Wang, Hong Wei ^a   Gu, Hong ^a  

^a DALIAN UNIVERSITY OF TECHNOLOGY   (China)

Author keywords

closed loop system; LQ decomposition; recursive algorithm; subspace identification method

Indexed keywords

ARRAY SIGNAL PROCESSING; EXPERIMENTAL CONDITIONS; LQ DECOMPOSITION; RECURSIVE ALGORITHMS; SLIDING WINDOW TECHNIQUES; SUBSPACE IDENTIFICATION; SUBSPACE IDENTIFICATION METHODS; SUBSPACE MODEL IDENTIFICATIONS;

ALGORITHMS; CLOSED LOOP SYSTEMS; IDENTIFICATION (CONTROL SYSTEMS); SIGNAL PROCESSING; VECTORS;

MATRIX ALGEBRA;

EID: 84890129961     PISSN: 13873954     EISSN: 17445051     Source Type: Journal    
DOI: 10.1080/13873954.2013.801355     Document Type: Article

References (14)

1. S.J. Qin, An overview of subspace identification, Comput. Chem. Eng. 30 (10-12) (2006), pp. 1502-1513.
2. P. Van Overschee and B. De Moor, Subspace Identification for Linear Systems: Theory, Implementation, Applications. Dordrecht, Kluwer Academic Publishers, 1996.
3. M. Gilson and G. Mercère. Subspace-based optimal IV method for closed-loop system identification, in Proceedings of the 14th IFAC Symposium on System Identification, IFAC, Newcastle, 29-31 March 2006, pp. 1068-1073.
4. P. Van Overschee. Closed loop subspace system identification, in Proceedings of the 36th IEEE Conference on Decision and Control, IEEE, San Diego, CA, 10-12 December 1997, pp. 1848-1853.
5. A. Chiuso and G. Picci, Consistency analysis of some closed-loop subspace identification methods, Automatica 41 (3) (2005), pp. 377-391.
6. T. Katayama, H. Kawauchi, and G. Picci, Subspace identification of closed-loop system by the orthogonal decomposition method, Automatica 41 (5) (2005), pp. 863-872.
7. G. Mercère, L. Bako, and S. Lecoeuche, Propagator-based methods for recursive subspace model identification, Signal Process. 88 (3) (2008), pp. 468-491.
8. L. Ljung, System Identification: Theory for the User, Prentice-Hall, Upper Saddle River, NJ, 2002.
9. T. Gustafsson, Instrumental variable subspace tracking using projection approximation. IEEE Trans. Signal Process. 46 (3) (1998), pp. 669-681.
10. P. Wu, C. Yang, and Z. Song, Recursive subspace model identification based on vector autoregressive modelling, in Proceedings of the 17th IFAC Symposium on Automatic Control, IFAC, Seoul, 6-11 July 2008, pp. 8872-8877.
11. I. Houtzager, J. Van Wingerden, and M. Verhaegen, Fast-array recursive closed-loop subspace model identification, in Proceedings of the 15th IFAC Symposium on System Identification, IFAC, Saint-Malo, 6-8 July 2009, pp. 96-101.
12. I. Markovsky, J.C. Willems, P. Rapisarda, and B. De Moor, Algorithm for deterministic balanced subspace identification, Automatica 41 (5) (2005), pp. 755-766.
13. T. Katayama and H. Tanaka, An approach to closed-loop subspace identification by orthogonal de-composition, Automatica 43 (2007), pp. 1623-1630.
14. G. Mercère, S. Lecoeuche, and C. Vasseur, A new recursive method for subspace identification of noisy systems: EIVPM, in Proceedings of the 13th IFAC Symposium on System Identification, Elsevier, Rotterdam, 27-29 August 2003, pp. 1637-1642.