Closed-loop subspace identification using the parity space

Jin WANG, S. Joe QIN*

*Corresponding author for this work

Research output: Journal PublicationsJournal Article (refereed)peer-review

100 Citations (Scopus)


It is known that many subspace algorithms give biased estimates for closed-loop data due to the existence of feedback. In this paper we present a new subspace identification method using the parity space employed in fault detection in the past. The basic algorithm, known as subspace identification method via principal component analysis (SIMPCA), gives consistent estimation of the deterministic part and stochastic part of the system under closed loop. Column weighting for SIMPCA is introduced which shows improved efficiency/accuracy. A simulation example is given to illustrate the performance of the proposed algorithm in closed-loop identification and the effect of column weighting. © 2005 Elsevier Ltd. All rights reserved.
Original languageEnglish
Pages (from-to)315-320
Number of pages6
Issue number2
Early online date20 Dec 2005
Publication statusPublished - Feb 2006
Externally publishedYes

Bibliographical note

Financial support from National Science Foundation under CTS-9985074 and an Overseas Young Investigator Award from NSF China (60228001) is gratefully acknowledged.


  • Closed-loop identification
  • Consistency analysis
  • Instrumental variables
  • Parity space
  • Subspace identification


Dive into the research topics of 'Closed-loop subspace identification using the parity space'. Together they form a unique fingerprint.

Cite this