IDEAS home Printed from https://ideas.repec.org/a/eee/apmaco/v456y2023ics0096300323003090.html
   My bibliography  Save this article

NNs-observer-based fully distributed consensus control for MASs under deception attacks

Author

Listed:
  • Zhang, Lei
  • Che, Wei-Wei
  • Ding, Jun-Hang

Abstract

This paper proposes a novel resilient intrusion-tolerance control scheme to solve the consensus control problem of multi-agent systems (MASs) under deception attacks. Consider the state unavailability of MASs with unknown nonlinear terms, the unmeasurable state is estimated by designing the neural-networks(NNs)-based observer. Then, a fully distributed intrusion-tolerant adaptive protocol is developed to implement the consensus control task. The designed control strategy only involves the agent states that interact with the neighboring agent and does not relate to the whole communication topology information. The designed controller can ensure that the consensus error of the nonlinear MASs is eventually ultimately bounded under deception attacks. Finally, a numerical simulation is provided to verify the feasibility of the designed scheme.

Suggested Citation

  • Zhang, Lei & Che, Wei-Wei & Ding, Jun-Hang, 2023. "NNs-observer-based fully distributed consensus control for MASs under deception attacks," Applied Mathematics and Computation, Elsevier, vol. 456(C).
  • Handle: RePEc:eee:apmaco:v:456:y:2023:i:c:s0096300323003090
    DOI: 10.1016/j.amc.2023.128140
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0096300323003090
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.amc.2023.128140?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    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:eee:apmaco:v:456:y:2023:i:c:s0096300323003090. 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.

    We have no bibliographic references for this item. You can help adding them by using 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: Catherine Liu (email available below). General contact details of provider: https://www.journals.elsevier.com/applied-mathematics-and-computation .

    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.