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Event-based joint state and fault estimation: The envelope-constrained H∞ criterion

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  • Wang, Licheng
  • Chen, Hui

Abstract

In this paper, the joint state and fault estimation problem is investigated for a class of discrete time-varying systems under the dynamic event-triggering mechanism. The actuator fault and the sensor fault are, respectively, considered in the state and measurement equations where only the changing rate of the actuator fault is needed. The dynamic event-triggering scheme is used to decrease unnecessary transmissions from the sensor to the estimator. To facilitate the joint estimator design, a novel descriptor system model is constructed by aggregating the system state and the sensor fault. The main aim of the addressed problem is to design a joint estimator to ensure that the estimation error dynamics simultaneously achieves the finite-horizon H∞ performance and the envelope constraint. Sufficient conditions are provided to obtain the desired joint estimator parameters by solving a series of recursive matrix inequalities. Finally, a numerical simulation example is given to demonstrate the effectiveness of the proposed estimation method. The major novelty of this study is that we make one of the very first few attempts to deal with the multi-objective joint state and fault estimation issue under the dynamic event-trigging transmission mechanism.

Suggested Citation

  • Wang, Licheng & Chen, Hui, 2022. "Event-based joint state and fault estimation: The envelope-constrained H∞ criterion," Applied Mathematics and Computation, Elsevier, vol. 419(C).
    • Handle: RePEc:eee:apmaco:v:419:y:2022:i:c:s0096300321009565
    DOI: 10.1016/j.amc.2021.126873
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    References listed on IDEAS

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    1. Zhongyi Zhao & Zidong Wang & Lei Zou & Jiyue Guo, 2020. "Set-membership filtering for time-varying complex networks with uniform quantisations over randomly delayed redundant channels," International Journal of Systems Science, Taylor & Francis Journals, vol. 51(16), pages 3364-3377, December.
    2. Hailong Tan & Bo Shen & Kaixiang Peng & Hongjian Liu, 2020. "Robust recursive filtering for uncertain stochastic systems with amplify-and-forward relays," International Journal of Systems Science, Taylor & Francis Journals, vol. 51(7), pages 1188-1199, May.
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