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Event-triggered state estimation for cyber-physical systems with partially observed injection attacks

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Conclusion

In this study, an event-based estimator with partially observed injection attacks is investigated. Under a specified transmission schedule and partially observed injection attacks, we prove that the conditional distribution of the state is Gaussian with a Bayesian inference approach. Then, an event-triggered minimum mean square error estimation algorithm is acquired. Moreover, we show that the proposed optimal estimator is exponentially stable under certain conditions.

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Acknowledgements

This work was supported by National Natural Science Foundation of China (Grant Nos. U21A20477, 61722302), Liaoning Revitalization Talents Program (Grant No. XLYC1907140), Fundamental Research Funds for the Central Universities (Grant No. DUT22ZD402), and National Science and Technology Major Project (Grant No. J2019-V-0010-0105)

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Authors and Affiliations

  1. The Key Laboratory of Intelligent Control and Optimization for Industrial Equipment (Dalian University of Technology), Ministry of Education, Dalian, 116000, China

    Le Liu, Xudong Zhao & Wei Xing

  2. School of Aerospace Science and Technology, Xidian University, Xi’an, 710010, China

    Bohui Wang

  3. School of Automation, Guangdong University of Technology, Guangzhou, 510006, China

    Yuanqing Wu

Authors
  1. Le Liu
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  2. Xudong Zhao
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  3. Bohui Wang
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  4. Yuanqing Wu
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  5. Wei Xing
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Corresponding author

Correspondence to Xudong Zhao.

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Liu, L., Zhao, X., Wang, B. et al. Event-triggered state estimation for cyber-physical systems with partially observed injection attacks. Sci. China Inf. Sci. 66, 169202 (2023). https://doi.org/10.1007/s11432-021-3260-0

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  • DOI: https://doi.org/10.1007/s11432-021-3260-0

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