Adaptive fuzzy consensus secure control of stochastic nonlinear multi-agent systems with false data injection attacks

Hui Li and Shaocheng Tong

International Journal of Systems Science, 2024, vol. 55, issue 6, 1191-1205

Abstract: This paper discusses the fuzzy adaptive consensus secure control problem for a class of stochastic nonlinear multi-agent systems (MASs) with unknown false data injection attacks. Fuzzy logic systems (FLSs) are used to model unknown nonlinear functions. A distributed filter is proposed to obtain the information of the leader since leaders information is only available to partial agents. To deal with the unknown virtual time-varying gains caused by false data injection attacks, an adaptive compensation method is developed via re-codesigning the virtual controllers. Based on backstepping control methodology and bounded estimation algorithms, an adaptive fuzzy consensus secure controller is proposed. It is proved that all the signals in the closed-loop systems are bounded in probability, and all the output of followers can track the leader under false data injection attacks. Finally, simulation results and comparative results show the effectiveness of the proposed control method.

Date: 2024
References: Add references at CitEc
Citations:

Downloads: (external link)
http://hdl.handle.net/10.1080/00207721.2024.2304121 (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:55:y:2024:i:6:p:1191-1205

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

DOI: 10.1080/00207721.2024.2304121

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 ().