IDEAS home Printed from https://ideas.repec.org/a/taf/tsysxx/v53y2022i8p1659-1673.html
   My bibliography  Save this article

Finite-time fault tolerant neural control of nonlinear multi-agent systems under switching topologies

Author

Listed:
  • Xiangying Guo
  • Jianyin Fang
  • Xiaowu Mu

Abstract

The neural tracking control problem for nonlinear strict-feedback multi-agent systems (MASs) subject to actuator faults in finite-time is considered, where each agent communicates with its neighbours on a time-varying directed topology. Radial basis function neural networks (RBFNNs) are used to approximate the unknown internal dynamics of system. By utilising backstepping method and dynamic surface control (DSC) technique, a novel adaptive fault tolerant controller is put forward. It turns out that the designed controller can compensate the effect of uncertainties and actuator faults. Meanwhile, by constructing piecewise Lyapunov functions, sufficient conditions for dwell time of topologies are given such that system achieves finite-time tracking consensus under the designed controller. Finally, an example is carried out to verify feasibility of the theoretical result.

Suggested Citation

  • Xiangying Guo & Jianyin Fang & Xiaowu Mu, 2022. "Finite-time fault tolerant neural control of nonlinear multi-agent systems under switching topologies," International Journal of Systems Science, Taylor & Francis Journals, vol. 53(8), pages 1659-1673, June.
    • Handle: RePEc:taf:tsysxx:v:53:y:2022:i:8:p:1659-1673
    DOI: 10.1080/00207721.2021.2019347
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1080/00207721.2021.2019347
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1080/00207721.2021.2019347?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.

    More about this item

    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:taf:tsysxx:v:53:y:2022:i:8:p:1659-1673. 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: Chris Longhurst (email available below). General contact details of provider: http://www.tandfonline.com/TSYS20 .

    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.