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Volume 11 Issue 5
May  2024

IEEE/CAA Journal of Automatica Sinica

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S. Wang, Z. Wang, H. Dong, Y. Chen, and  G. Lu,  “Dynamic event-triggered quadratic nonfragile filtering for non-Gaussian systems: Tackling multiplicative noises and missing measurements,” IEEE/CAA J. Autom. Sinica, vol. 11, no. 5, pp. 1127–1138, May 2024. doi: 10.1109/JAS.2024.124338
Citation: S. Wang, Z. Wang, H. Dong, Y. Chen, and  G. Lu,  “Dynamic event-triggered quadratic nonfragile filtering for non-Gaussian systems: Tackling multiplicative noises and missing measurements,” IEEE/CAA J. Autom. Sinica, vol. 11, no. 5, pp. 1127–1138, May 2024. doi: 10.1109/JAS.2024.124338

Dynamic Event-Triggered Quadratic Nonfragile Filtering for Non-Gaussian Systems: Tackling Multiplicative Noises and Missing Measurements

doi: 10.1109/JAS.2024.124338
Funds:  This work was supported in part by the National Natural Science Foundation of China (61933007, U21A2019, U22A2044, 61973102, 62073180), the Natural Science Foundation of Shandong Province of China (ZR2021MF088), the Hainan Province Science and Technology Special Fund of China (ZDYF2022SHFZ105), the Royal Society of the UK, and the Alexander von Humboldt Foundation of Germany
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  • This paper focuses on the quadratic nonfragile filtering problem for linear non-Gaussian systems under multiplicative noises, multiple missing measurements as well as the dynamic event-triggered transmission scheme. The multiple missing measurements are characterized through random variables that obey some given probability distributions, and thresholds of the dynamic event-triggered scheme can be adjusted dynamically via an auxiliary variable. Our attention is concentrated on designing a dynamic event-triggered quadratic nonfragile filter in the well-known minimum-variance sense. To this end, the original system is first augmented by stacking its state/measurement vectors together with second-order Kronecker powers, thus the original design issue is reformulated as that of the augmented system. Subsequently, we analyze statistical properties of augmented noises as well as high-order moments of certain random parameters. With the aid of two well-defined matrix difference equations, we not only obtain upper bounds on filtering error covariances, but also minimize those bounds via carefully designing gain parameters. Finally, an example is presented to explain the effectiveness of this newly established quadratic filtering algorithm.

     

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    Highlights

    • he quadratic non-Gaussian nonfragile filter is devised firstly under under MNs, DETS, and MMMs
    • The statistics of augmented noises and involved random variables are revealed
    • A new quadratic nonfragile filter is designed by minimizing upper bounds on filtering error covariances

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