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

Xiangying Guo, Jianyin Fang and Xiaowu Mu

International Journal of Systems Science, 2022, vol. 53, issue 8, 1659-1673

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.

Date: 2022
References: Add references at CitEc
Citations:

Downloads: (external link)
http://hdl.handle.net/10.1080/00207721.2021.2019347 (text/html)
Access to full text is restricted to subscribers.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:taf:tsysxx:v:53:y:2022:i:8:p:1659-1673

Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/TSYS20

DOI: 10.1080/00207721.2021.2019347

Access Statistics for this article

International Journal of Systems Science is currently edited by Visakan Kadirkamanathan

More articles in International Journal of Systems Science from Taylor & Francis Journals
Bibliographic data for series maintained by Chris Longhurst ().