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

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AsiaSim 2012 (AsiaSim 2012)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 323))

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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.

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Author information

Authors and Affiliations

  1. Department of Automation, School of Mechatronics Engineering and Automation, Shanghai Key Laboratory of Power Station Automation Technology, Shanghai University, Shanghai, 200072, China

    Jianguo Wang, Yong Guo & Juanjuan Wang

Authors
  1. Jianguo Wang
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  2. Yong Guo
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  3. Juanjuan Wang
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Editor information

Editors and Affiliations

  1. National CIMS Engineering Research Center, Department of Automation, Tsinghua University, 100084, Beijing, China

    Tianyuan Xiao

  2. School of Automation Science and Electrical Engineering, Beihang University, 100191, Beijing, China

    Lin Zhang

  3. School of Mechatronics Engineering and Automation, Shanghai University, 200072, Shanghai, China

    Minrui Fei

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© 2012 Springer-Verlag Berlin Heidelberg

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Wang, J., Guo, Y., Wang, J. (2012). Closed-Loop Subspace Identification Algorithm of EIV Model Based on Orthogonal Decomposition and PCA. In: Xiao, T., Zhang, L., Fei, M. (eds) AsiaSim 2012. AsiaSim 2012. Communications in Computer and Information Science, vol 323. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-34384-1_8

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  • DOI: https://doi.org/10.1007/978-3-642-34384-1_8

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-34383-4

  • Online ISBN: 978-3-642-34384-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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