IDEAS home Printed from https://ideas.repec.org/a/gam/jmathe/v11y2023i9p1997-d1130875.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-7390/11/9/1997/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2227-7390/11/9/1997/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    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).
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Luo, Mei & Wang, JinRong & Meng, Deyuan, 2023. "Stochastic convergence problems on switching networks: An event-triggered method," Chaos, Solitons & Fractals, Elsevier, vol. 170(C).
    2. Miao, Suoxia & Su, Housheng, 2024. "Behaviors of matrix-weighted networks with antagonistic interactions," Applied Mathematics and Computation, Elsevier, vol. 467(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jmathe:v:11:y:2023:i:9:p:1997-:d:1130875. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.