Advanced search
Publication Cover
Mathematical and Computer Modelling of Dynamical Systems
Methods, Tools and Applications in Engineering and Related Sciences
Volume 19, 2013 - Issue 6
Submit an article Journal homepage
376
Views
5
CrossRef citations to date
0
Altmetric
Original Articles

A novel recursive subspace identification approach of closed-loop systems

Jia WangSchool of Control Science and Engineering, Dalian University of Technology, Dalian, China
,
Hong-Wei WangSchool of Control Science and Engineering, Dalian University of Technology, Dalian, China
&
Hong GuSchool of Control Science and Engineering, Dalian University of Technology, Dalian, ChinaCorrespondence[email protected]
Pages 526-539 | Received 01 Dec 2012, Accepted 29 Apr 2013, Published online: 22 May 2013

References

  • S.J. Qin, An overview of subspace identification, Comput. Chem. Eng. 30 (10–12) (2006), pp. 1502–1513.
  • P. Van Overschee and B. De Moor, Subspace Identification for Linear Systems: Theory, Implementation, Applications. Dordrecht, Kluwer Academic Publishers, 1996.
  • 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.
  • 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.
  • A. Chiuso and G. Picci, Consistency analysis of some closed-loop subspace identification methods, Automatica 41 (3) (2005), pp. 377–391.
  • 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.
  • G. Mercère, L. Bako, and S. Lecoeuche, Propagator-based methods for recursive subspace model identification, Signal Process. 88 (3) (2008), pp. 468–491.
  • L. Ljung, System Identification: Theory for the User, Prentice-Hall, Upper Saddle River, NJ, 2002.
  • T. Gustafsson, Instrumental variable subspace tracking using projection approximation. IEEE Trans. Signal Process. 46 (3) (1998), pp. 669–681.
  • 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.
  • 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.
  • I. Markovsky, J.C. Willems, P. Rapisarda, and B. De Moor, Algorithm for deterministic balanced subspace identification, Automatica 41 (5) (2005), pp. 755–766.
  • T. Katayama and H. Tanaka, An approach to closed-loop subspace identification by orthogonal de-composition, Automatica 43 (2007), pp. 1623–1630.
  • G. Mercè re, 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.

Reprints and Corporate Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

To request a reprint or corporate permissions for this article, please click on the relevant link below:

Order Reprints Request Corporate Permissions

Academic Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

Obtain permissions instantly via Rightslink by clicking on the button below:

Request Academic Permissions

If you are unable to obtain permissions via Rightslink, please complete and submit this Permissions form. For more information, please visit our Permissions help page.

Related research

People also read lists articles that other readers of this article have read.

Recommended articles lists articles that we recommend and is powered by our AI driven recommendation engine.

Cited by lists all citing articles based on Crossref citations.
Articles with the Crossref icon will open in a new tab.