A time-varying gain method to consensus control of high-order nonlinear multi-agent systems with input saturations
Hanfeng Li and
Min Li
International Journal of Systems Science, 2025, vol. 56, issue 10, 2357-2369
Abstract:
This paper introduces a fresh time-varying gain method to tackle the leader–follower consensus issue of high-order nonlinear multi-agent systems while taking into account input saturations. The nonlinear characteristics of each agent are structured in a triangular pattern and are susceptible to influence from unspecified external disturbances. Within the framework of the lower triangular structure, a compensator with time-varying gain is tailored for each agent. This compensator is established by the output of the follower and its neighbouring agents. By utilising a directed communication topology, a distributed output feedback control protocol is crafted for the system at hand. This protocol ensures that consensus errors gradually converge towards a customisable bounded region. Moreover, the time-varying gain method can be extended to achieve consensus control for multi-agent systems, where each agent is characterised by an upper triangular structure. The effectiveness of the proposed distributed control protocols is shown by two simulation examples.
Date: 2025
References: Add references at CitEc
Citations:
Downloads: (external link)
http://hdl.handle.net/10.1080/00207721.2024.2447881 (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:10:p:2357-2369
Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/TSYS20
DOI: 10.1080/00207721.2024.2447881
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 ().