IDEAS home Printed from https://ideas.repec.org/a/eee/apmaco/v515y2026ics0096300325005673.html

Consensus of multi-agent systems under variable denial-of-service attacks: Noise-based event-triggered protocols

Author

Listed:
  • Li, Sen
  • Gao, Yihang
  • Li, Wenxue

Abstract

This paper unveils the consensus of multi-agent systems (MSs) with noise under variable denial-of-service attacks (VDoSA). Instead of the current continuous observation noise, a noise-based dynamic event-triggered protocol (NDETP) is raised to assure the consensus. It is worth emphasizing that we focus on almost sure consensus, that is, noise plays a constructive role in achieving consensus, which protests about the present results regarding stochastic MSs with event-triggered strategy. Owing to the distinctiveness of the problem, a novel comparison method integrating Lyapunov method are adopted since directly using the Lyapunov method is no longer applicable. By analyzing the convergence of the solution in small order moments for the system with continuous-time consensus protocol, an almost sure consensus criterion of MSs is derived directly that removes the prerequisite of mean square consensus. The criterion is closely associated with topological structure, attack successful probability, average attacks rate, and noise magnitude. Based on the established consensus criterion, an algorithm is given to settle the solutions of related parameters in event-triggering mechanisms while ensuring the consensus. Eventually, the theoretical results are used to investigate a class of robot arms system with a numerical example being shown.

Suggested Citation

  • Li, Sen & Gao, Yihang & Li, Wenxue, 2026. "Consensus of multi-agent systems under variable denial-of-service attacks: Noise-based event-triggered protocols," Applied Mathematics and Computation, Elsevier, vol. 515(C).
  • Handle: RePEc:eee:apmaco:v:515:y:2026:i:c:s0096300325005673
    DOI: 10.1016/j.amc.2025.129842
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0096300325005673
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.amc.2025.129842?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

    for a different version of it.

    More about this item

    Keywords

    ;
    ;
    ;
    ;

    Statistics

    Access and download statistics

    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:515:y:2026:i:c:s0096300325005673. 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.