IDEAS home Printed from https://ideas.repec.org/a/eee/apmaco/v495y2025ics0096300325000633.html
   My bibliography  Save this article

Mode-based triggered protocol for synchronization of switched dynamic networks with semi-Markov kernel and its application

Author

Listed:
  • Chen, Mengshen
  • Ding, Derui
  • Yan, Huaicheng
  • Liang, Kun

Abstract

In this paper, the mean square synchronization is investigated for discrete-time dynamic networks with stochastic switching topologies via a semi-Markov kernel approach. A novel mode-based triggered protocol is developed to reduce the communication frequency, where the data between coupled nodes is only transmitted at the topology switching instants. Besides, a more general semi-Markov chain based on the semi-Markov kernel approach is first adopted to describe the stochastic switching process of network topologies, then the different types of probability density functions can coexist in any topological mode. Under the proposed triggering strategy, a mode-based distributed synchronization protocol is formed to deal with the controller design difficulty caused by the randomness of semi-Markov switching. Based on the Lyapunov function, some auxiliary inequalities in existing results are avoided and further the numerically testable criterion is obtained to ensure mean square synchronization of the dynamic networks. Finally, a practical example of power systems is presented to demonstrate the effectiveness of the proposed method.

Suggested Citation

  • Chen, Mengshen & Ding, Derui & Yan, Huaicheng & Liang, Kun, 2025. "Mode-based triggered protocol for synchronization of switched dynamic networks with semi-Markov kernel and its application," Applied Mathematics and Computation, Elsevier, vol. 495(C).
  • Handle: RePEc:eee:apmaco:v:495:y:2025:i:c:s0096300325000633
    DOI: 10.1016/j.amc.2025.129336
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0096300325000633
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.amc.2025.129336?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:495:y:2025:i:c:s0096300325000633. 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.