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Torus-Event-Based Fault Estimation for Stochastic Nonlinear Systems with Randomly Occurring Saturation and Missing Measurements

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Abstract

This paper is aiming at the fault estimation issue within finite time domain for a type of discrete-time stochastic nonlinear systems. The considered sensor network is affected by randomly occurring sensor saturation and missing measurements. A sensor model is established based on Bernoulli distributions of two known probabilities, which can describe the two types of random incomplete information in a unified framework. Additionally, in an effort to alleviate communication load and maintain data security, the torus-event-based strategy is employed to schedule output data in network control systems. On this basis, a suitable estimator is developed, and sufficient conditions for a fault estimator with reduced conservatism are presented. These conditions guarantee that the dynamic error of the designed estimator meets predetermined performance requirements, and the corresponding fault estimator gains are designed accordingly. Finally, simulation examples are used to testify that the considered estimation algorithm can maintain good estimation performance under torus-event-based mechanisms in complex networks.

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The authors declare that all data and materials including custom code support the work claimed in the manuscript are available from either the corresponding author or the first author on reasonable request.

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Acknowledgements

This work was supported in part by the National Natural Science Foundation of China under Grant 62073189 and Grant 62173207, and in part by the Taishan Scholar Project of Shandong Province under Grant tsqn202211129.

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  1. Institute of Automation, Qufu Normal University, Qufu, 273165, China

    Xinci Gao, Weiwei Sun, Xiangyu Chen & Lusong Ding

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  1. Xinci Gao
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  2. Weiwei Sun
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  3. Xiangyu Chen
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Correspondence to Weiwei Sun.

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Gao, X., Sun, W., Chen, X. et al. Torus-Event-Based Fault Estimation for Stochastic Nonlinear Systems with Randomly Occurring Saturation and Missing Measurements. Circuits Syst Signal Process 43, 4790–4812 (2024). https://doi.org/10.1007/s00034-024-02686-2

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