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Distributed Moving Horizon Fusion Estimation for Nonlinear Constrained Uncertain Systems

Author

Listed:
  • Shoudong Wang

    (School of Automation, Qingdao University, Qingdao 266100, China
    These authors contributed equally to this work.)

  • Binqiang Xue

    (School of Automation, Qingdao University, Qingdao 266100, China
    These authors contributed equally to this work.)

Abstract

This paper studies the state estimation of a class of distributed nonlinear systems. A new robust distributed moving horizon fusion estimation (DMHFE) method is proposed to deal with the norm-bounded uncertainties and guarantee the estimation performance. Based on the given relationship between a state covariance matrix and an error covariance matrix, estimated values of the unknown parameters in the system model can be obtained. Then, a local moving horizon estimation optimization algorithm is constructed by using the measured values of sensor nodes themselves, the measured information of adjacent nodes and the prior state estimates. By solving the above nonlinear optimization problem, a local optimal state estimation is obtained. Next, based on covariance intersection (CI) fusion strategy, the local optimal state estimates sent to the fusion center are fused to derive optimal state estimates. Furthermore, the sufficient conditions for the square convergence of the fusion estimation error norm are given. Finally, a simulation example is employed to demonstrate the effectiveness of the proposed algorithm.

Suggested Citation

  • Shoudong Wang & Binqiang Xue, 2023. "Distributed Moving Horizon Fusion Estimation for Nonlinear Constrained Uncertain Systems," Mathematics, MDPI, vol. 11(6), pages 1-22, March.
    • Handle: RePEc:gam:jmathe:v:11:y:2023:i:6:p:1507-:d:1102400
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    Cited by:

    1. Zun Li & Binqiang Xue & Youyuan Chen, 2023. "Event-Triggered State Estimation for Uncertain Systems with Binary Encoding Transmission Scheme," Mathematics, MDPI, vol. 11(17), pages 1-16, August.

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