EconPapers    
Economics at your fingertips  
 

Event-based finite-time sliding mode consensus tracking control for multiagent systems with actuator failures

Lufu Zheng, Hongru Ren, Deyin Yao and Hongyi Li

International Journal of Systems Science, 2025, vol. 56, issue 2, 303-317

Abstract: This paper investigates the finite-time sliding mode control problem for multiagent systems with actuator failures and external disturbances. Given the presence of unknown nonlinear terms in the controlled multiagent systems, a radial basis function neural network is employed to ensure system robustness. To achieve consensus tracking of sliding mode dynamics within a finite-time, a finite-time integral sliding manifold is proposed. An adaptive law is designed to estimate the unknown fault coefficient, and then a distributed event-triggered adaptive sliding mode fault-tolerant control protocol is developed to deal with external disturbances and actuator faults in multiagent systems, which can effectively reduce the communication bandwidth and enhance reliability against actuator faults. To verify the effectiveness of the proposed control method, a numerical example is provided.

Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
http://hdl.handle.net/10.1080/00207721.2024.2392836 (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:56:y:2025:i:2:p:303-317

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

DOI: 10.1080/00207721.2024.2392836

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 ().

 
Page updated 2025-03-20
Handle: RePEc:taf:tsysxx:v:56:y:2025:i:2:p:303-317