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Mobile Sensor Networks for Finite-Time Distributed H ∞ Consensus Filtering of 3D Nonlinear Distributed Parameter Systems with Randomly Occurring Sensor Saturation

Author

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  • Xueming Qian

    (School of Internet of Things, Wuxi Vocational College of Science and Technology, Wuxi 214028, China
    School of Internet of Things Engineering, Jiangnan University, Wuxi 214122, China)

  • Baotong Cui

    (School of Internet of Things Engineering, Jiangnan University, Wuxi 214122, China)

Abstract

This paper is concerned with designing a distributed bounded H ∞ consensus filter to estimate an array of three-dimensional (3D) nonlinear distributed parameter systems subject to bounded perturbation. An optimization framework based on mobile sensing is proposed to improve the performance of distributed filters. The measurement output is obtained from a mobile sensor network, where a phenomenon of randomly occurring sensor saturation is taken into account to reflect the reality in a mobile networked environment. A sufficient condition is established by utilizing operator-dependent Lyapunov functional for the filtering error system to be finite-time bounded. Note that the velocity law of each mobile sensor is included in this condition. The effect from the exogenous perturbation to the estimation accuracy is guaranteed at a given level by means of H ∞ consensus performance constraint. Finally, simulation examples are presented to demonstrate the applicability of the theoretical results.

Suggested Citation

  • Xueming Qian & Baotong Cui, 2022. "Mobile Sensor Networks for Finite-Time Distributed H ∞ Consensus Filtering of 3D Nonlinear Distributed Parameter Systems with Randomly Occurring Sensor Saturation," Mathematics, MDPI, vol. 10(17), pages 1-24, September.
    • Handle: RePEc:gam:jmathe:v:10:y:2022:i:17:p:3134-:d:903785
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    References listed on IDEAS

    as
    1. Zhu, Fengzeng & Liu, Xu & Peng, Li, 2021. "Adaptive consensus-based distributed H∞ filtering with switching topology subject to partial information on transition probabilities," Applied Mathematics and Computation, Elsevier, vol. 411(C).
    2. Yan, Zhilian & Guo, Tong & Zhao, Anqi & Kong, Qingkai & Zhou, Jianping, 2022. "Reliable exponential H∞ filtering for a class of switched reaction-diffusion neural networks," Applied Mathematics and Computation, Elsevier, vol. 414(C).
    3. Wei Guan & Lei Fu & Yuechao Ma, 2019. "Finite-Time Filtering for Discrete-Time Singular Markovian Jump Systems with Time Delay and Input Saturation," Complexity, Hindawi, vol. 2019, pages 1-22, April.
    4. Liang Chen & Peng Jin & Jing Yang & Yang Li & Yi Song, 2021. "Robust Kalman Filter-Based Dynamic State Estimation of Natural Gas Pipeline Networks," Mathematical Problems in Engineering, Hindawi, vol. 2021, pages 1-10, March.
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