A distributed adaptive architecture with the nonlinear reference model for safe finite-time control of uncertain multiagent systems

Meryem Deniz, K. Merve Dogan and Tansel Yucelen

International Journal of Systems Science, 2023, vol. 54, issue 4, 822-834

Abstract: We propose a new distributed adaptive control architecture for finite-time control of uncertain nonlinear multiagent systems. The proposed architecture employs three key components for each agent; a nonlinear reference model, a weight update rule, and an adaptive control signal. Predicated on agent-wise reference model state exchange, an ideal finite-time behaviour of overall multiagent system is captured by nonlinear reference models. The weight update rule of each agent, which is driven by a local error signal between the actual uncertain state of an agent and its reference model state, then adjusts agent-wise controller parameters in real-time to drive the local error signals of each agent to zero in finite-time. That is, not only the states of agents converge to their nonlinear reference model states in finite-time, but also the latter states converge to the given ideal behaviour in finite-time. Considering safety, the distinct feature of our architecture is that it does not rely on agent-wise actual state exchange between agents, which involves the effect of system uncertainties. This implies that when a subset of agents exhibits, for example, Byzantine behaviour, then their behaviour do not affect the rest of multiagent system from functioning.

Date: 2023
References: Add references at CitEc
Citations:

Downloads: (external link)
http://hdl.handle.net/10.1080/00207721.2022.2146988 (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:54:y:2023:i:4:p:822-834

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

DOI: 10.1080/00207721.2022.2146988

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 ().