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The Dynamic Event-Based Non-Fragile H ∞ State Estimation for Discrete Nonlinear Systems with Dynamical Bias and Fading Measurement

Author

Listed:
  • Manman Luo

    (School of Science, Jiangnan University, Wuxi 214122, China)

  • Baibin Yang

    (School of Science, Jiangnan University, Wuxi 214122, China)

  • Zhaolei Yan

    (School of Science, Jiangnan University, Wuxi 214122, China)

  • Yuwen Shen

    (School of Science, Jiangnan University, Wuxi 214122, China)

  • Manfeng Hu

    (School of Science, Jiangnan University, Wuxi 214122, China)

Abstract

The present study investigates non-fragile H ∞ state estimation based on a dynamic event-triggered mechanism for a class of discrete time-varying nonlinear systems subject to dynamical bias and fading measurements. The dynamic deviation caused by unknown inputs is represented by a dynamic equation with bounded noise. Subsequently, the augmentation technique is employed and the dynamic event-triggered mechanism is introduced in the sensor-to-estimator channel to determine whether data should be transmitted or not, thereby conserving resources. Furthermore, an augmented state-dependent non-fragile state estimator is constructed considering gain perturbation of the estimator and fading measurements during network transmission. Sufficient conditions are provided based on Lyapunov stability and matrix analysis techniques to ensure exponential mean-square stability of the estimation error system while satisfying the H ∞ disturbance fading level. The desired estimator gain matrix can be obtained by solving the linear matrix inequality (LMI). Finally, an example is presented to illustrate the effectiveness of the proposed method for designing estimators.

Suggested Citation

  • Manman Luo & Baibin Yang & Zhaolei Yan & Yuwen Shen & Manfeng Hu, 2024. "The Dynamic Event-Based Non-Fragile H ∞ State Estimation for Discrete Nonlinear Systems with Dynamical Bias and Fading Measurement," Mathematics, MDPI, vol. 12(18), pages 1-16, September.
    • Handle: RePEc:gam:jmathe:v:12:y:2024:i:18:p:2957-:d:1483958
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    References listed on IDEAS

    as
    1. Meiyu Li & Jinling Liang & Fan Wang, 2022. "Robust set-membership filtering for two-dimensional systems with sensor saturation under the Round-Robin protocol," International Journal of Systems Science, Taylor & Francis Journals, vol. 53(13), pages 2773-2785, October.
    2. Xin Wang & Edwin E. Yaz, 2014. "Stochastically resilient extended Kalman filtering for discrete-time nonlinear systems with sensor failures," International Journal of Systems Science, Taylor & Francis Journals, vol. 45(7), pages 1393-1401, July.
    3. Li, Ruoxia & Gao, Xingbao & Cao, Jinde, 2019. "Non-fragile state estimation for delayed fractional-order memristive neural networks," Applied Mathematics and Computation, Elsevier, vol. 340(C), pages 221-233.
    4. Fanrong Qu & Xia Zhao & Xinmeng Wang & Engang Tian, 2022. "Probabilistic-constrained distributed fusion filtering for a class of time-varying systems over sensor networks: a torus-event-triggering mechanism," International Journal of Systems Science, Taylor & Francis Journals, vol. 53(6), pages 1288-1297, April.
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