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Closed-Loop Subspace Identification Algorithm of EIV Model Based on Orthogonal Decomposition and PCA

  • Jianguo Wang
  • Yong Guo
  • Juanjuan Wang
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 323)

Abstract

In this paper, after analysis of the reason why some existing subspace methods may deliver a bias in the closed-loop conditions, a new SIM for closed-loop system based on orthogonal decomposition and principal component analysis is proposed by adopting the EIV model structure. Then, the underlying reason why SIMPCA-Wc delivers a bias estimate is explained from realization theory of closed-loop system based on orthogonal decomposition. At last, simulations show that the proposed method ORT_PCA-Wc used for closed-loop EIV system is effective and feasible.

Keywords

subspace identification methods (SIMs) closed-loop identification orthogonal decomposition principal component analysis (PCA) error in variable (EIV) 

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References

  1. 1.
    Ljung, L., McKelvey, T.: Subspace identification from closed loop data. Signal Processing 52, 209–215 (1996)zbMATHCrossRefGoogle Scholar
  2. 2.
    Jansson, M.: Subspace identification and ARX modeling. In: Proceedings of the 13th IFAC SYSID Symposium (2003)Google Scholar
  3. 3.
    Qin, S.J., Ljung, L.: Closed-loop subspace identification with innovation estimation. In: Proceedings of SYSID 2003, Rotterdam (2003)Google Scholar
  4. 4.
    Chiuso, A., Picci, G.: Consistency analysis of some closed-loop subspace identification. Automatica 41(3), 377–391 (2005)zbMATHMathSciNetCrossRefGoogle Scholar
  5. 5.
    Wang, J., Qin, S.J.: A new subspace identification approach based on principal component analysis. Journal of Process Control 12(8), 841–845 (2002)MathSciNetCrossRefGoogle Scholar
  6. 6.
    Huang, B., Ding, S.X., Qin, S.: Closed-loop subspace identification: an orthogonal Projection approach. Journal of Process Control 15, 53–66 (2005)CrossRefGoogle Scholar
  7. 7.
    Katayama, T., Kawauchi, H., Picci, G.: Subspace identification of closed-loop systems by orthogonal projection method. Automatica 41(5), 863–872 (2005)zbMATHMathSciNetCrossRefGoogle Scholar
  8. 8.
    Wang, J., Qin, S.: Closed-loop subspace identification using the parity space. Automatica 42, 315–320 (2006)zbMATHMathSciNetCrossRefGoogle Scholar
  9. 9.
    Katayama, T., Tanaka, H.: An approach to closed-loop subspace identification by orthogonal decomposition. Automatica 43, 1623–1630 (2007)zbMATHMathSciNetCrossRefGoogle Scholar
  10. 10.
    Chou, C., Verhaegen, M.: Subspace algorithms for the identification of multivariable dynamic errors-in-variables models. Automatica 33(10), 1857–1869 (1997)zbMATHMathSciNetCrossRefGoogle Scholar
  11. 11.
    Wang, J., Qin, S.J.: Principal component analysis for errors-in-variables subspace identification. In: Proceedings of the 40th IEEE Conference on Decision and Control, Orlando, Florida, pp. 3934–3941 (2001)Google Scholar
  12. 12.
    Van Den Hof, P., Schrama, R.: An indirect method for transfer function estimation from closed loop data. Automatica 29(6), 1523–1527 (1993)zbMATHMathSciNetCrossRefGoogle Scholar
  13. 13.
    Katayama, T., Picci, G.: Realization of stochastic systems with exogenous inputs and subspace identification methods. Automatica 35(10), 1635–1652 (1999)zbMATHMathSciNetCrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Jianguo Wang
    • 1
  • Yong Guo
    • 1
  • Juanjuan Wang
    • 1
  1. 1.Department of Automation, School of Mechatronics Engineering and Automation, Shanghai Key Laboratory of Power Station Automation TechnologyShanghai UniversityShanghaiChina

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