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Event-Triggered Fault-Detection Filter Using Coordinate Transformation Approach for Time-Varying Stochastic Systems

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Security, Privacy, and Anonymity in Computation, Communication, and Storage (SpaCCS 2018)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 11342))

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Abstract

In this paper, an event-triggered fault-detection filter design for reducing communication actions in networked time-varying stochastic systems over a finite-time horizon is proposed. A coordinate transformation approach is exploited to achieve the purpose that the fault-detection residual is only sensitive to system faults while robust to additive unknown disturbances. This approach can transform the considered system into two subsystems, and the disturbances are removed from one of the subsystems. Furthermore, the optimal gain for each filter is derived, which applies to the optimization criteria of unbiasedness and minimum mean-square estimation error.

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Author information

Authors and Affiliations

  1. Engineering Research Center of Internet of Things Technology Applications (Ministry of Education), Jiangnan University, Wuxi, 214122, China

    Yunji Li & Li Peng

  2. Wuxi High Electromechanical Technology Vocational School, Wuxi, 214028, China

    Xuefang Zhu

  3. School of Mechanical Engineering, Jiangnan University, Wuxi, 214122, China

    Wen Li

Authors
  1. Yunji Li
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  2. Li Peng
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  3. Xuefang Zhu
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  4. Wen Li
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Corresponding author

Correspondence to Li Peng .

Editor information

Editors and Affiliations

  1. Guangzhou University, Guangzhou, China

    Guojun Wang

  2. Swinburne University of Technology, Melbourne, VIC, Australia

    Jinjun Chen

  3. St. Francis Xavier University, Antigonish, NS, Canada

    Laurence T. Yang

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Li, Y., Peng, L., Zhu, X., Li, W. (2018). Event-Triggered Fault-Detection Filter Using Coordinate Transformation Approach for Time-Varying Stochastic Systems. In: Wang, G., Chen, J., Yang, L. (eds) Security, Privacy, and Anonymity in Computation, Communication, and Storage. SpaCCS 2018. Lecture Notes in Computer Science(), vol 11342. Springer, Cham. https://doi.org/10.1007/978-3-030-05345-1_10

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  • DOI: https://doi.org/10.1007/978-3-030-05345-1_10

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-05344-4

  • Online ISBN: 978-3-030-05345-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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