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H∞ state estimation for multiplex networks with randomly occurring sensor saturations

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  • Wu, Xifen
  • Bao, Haibo

Abstract

This paper investigates the H∞ state estimation problem of multiplex networks (MNs) with sensor saturations. Sensor saturations are first introduced into MNs to study the state estimation of MNs. The aim of this paper is to design a set of H∞ state estimators to estimate the state of MNs through the available output measurements. The basic requirements of this kind of estimators are that the dynamics of estimation error is exponentially mean-square stable and meets the H∞ performance requirement. The sufficient conditions are established to meet these two basic requirements. In this paper, the normal operation, sensor saturations and missing measurements of MNs are described in a unified way by means of Kronecker delta function, and a new measurement model is proposed to describe these random events. This model can clearly and intuitively explain any combination of the normal operation, sensor saturations and missing measurements only by changing the probability of their random occurrence. Next, the estimator gain of the designed estimators for MNs can be easily obtained by taking advantage of solving certain matrix inequalities. Through a series of analysis, it is found that there is an important relationship between the inter-layer couplings of MNs and the estimation time of MNs. Finally, the effectiveness of the proposed state estimation approach is verified by numerical simulations.

Suggested Citation

  • Wu, Xifen & Bao, Haibo, 2023. "H∞ state estimation for multiplex networks with randomly occurring sensor saturations," Applied Mathematics and Computation, Elsevier, vol. 437(C).
    • Handle: RePEc:eee:apmaco:v:437:y:2023:i:c:s0096300322006129
    DOI: 10.1016/j.amc.2022.127538
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    References listed on IDEAS

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    1. Song, Xinmin & Duan, Zhenhua & Park, Ju H., 2016. "Linear optimal estimation for discrete-time systems with measurement-delay and packet dropping," Applied Mathematics and Computation, Elsevier, vol. 284(C), pages 115-124.
    2. Dai, Shifang & Zha, Lijuan & Liu, Jinliang & Xie, Xiangpeng & Tian, Engang, 2022. "Fault detection filter design for networked systems with cyber attacks," Applied Mathematics and Computation, Elsevier, vol. 412(C).
    3. Liu, Jinliang & Xia, Jilei & Tian, Engang & Fei, Shumin, 2018. "Hybrid-driven-based H∞ filter design for neural networks subject to deception attacks," Applied Mathematics and Computation, Elsevier, vol. 320(C), pages 158-174.
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