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Distributed Observers for State Omniscience with Stochastic Communication Noises

Author

Listed:
  • Kairui Chen

    (School of Mechanical and Electrical Engineering, Guangzhou University, Guangzhou 510006, China
    School of Computer & Information, Qiannan Normal University for Nationalities, Duyun 558000, China)

  • Zhangmou Zhu

    (School of Mechanical and Electrical Engineering, Guangzhou University, Guangzhou 510006, China)

  • Xianxian Zeng

    (School of Computer Science, Guangdong Polytechnic Normal University, Guangzhou 510000, China)

  • Junwei Wang

    (School of Mathematics and Statistics, Guangdong University of Foreign Studies, Guangzhou 510006, China)

Abstract

The focus of this paper is on solving the state estimation problem for general continuous-time linear systems through the use of distributed networked observers. To better reflect the communication environment, stochastic noises are considered when observers exchange information. In the networked observers, each local observer measures only part of the system output, and the state estimation can not be accomplished within a single observer. Then, all observers communicate through a pre-specified graph to make up information in the remaining system output. By solving a parametric algebraic Riccati equation (ARE), a simple method to calculate parameters in the observers is proposed. Furthermore, using the stability theory of stochastic differential equations, state omniscience is discussed in almost sure sense and in the mean square sense for the cases of state-dependent noises and non-state-dependent noises, respectively. It is shown that, for observable linear systems, the resulting observers work in a coordinated mode to reach state omniscience under the connected graph. Illustrative examples are provided to show the effectiveness of the distributed observers.

Suggested Citation

  • Kairui Chen & Zhangmou Zhu & Xianxian Zeng & Junwei Wang, 2023. "Distributed Observers for State Omniscience with Stochastic Communication Noises," Mathematics, MDPI, vol. 11(9), pages 1-14, April.
    • Handle: RePEc:gam:jmathe:v:11:y:2023:i:9:p:1997-:d:1130875
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    References listed on IDEAS

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    1. Du, Yingxue & Wang, Yijing & Zuo, Zhiqiang & Zhang, Wentao, 2022. "Event-triggered bipartite consensus for multi-agent systems subject to multiplicative and additive noises," Applied Mathematics and Computation, Elsevier, vol. 429(C).
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    Cited by:

    1. Patricio Borbolla-Burillo & David Sotelo & Michael Frye & Luis E. Garza-Castañón & Luis Juárez-Moreno & Carlos Sotelo, 2024. "Design and Real-Time Implementation of a Cascaded Model Predictive Control Architecture for Unmanned Aerial Vehicles," Mathematics, MDPI, vol. 12(5), pages 1-20, February.

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