Adaptive leaderless consensus of agents in jointly connected networks

dc.contributor.authorYu, Hui
dc.contributor.authorXia, Xiaohua
dc.date.accessioned2017-05-12T09:56:42Z
dc.date.issued2017-06
dc.description.abstractIn this paper, the leaderless consensus problem of multi-agent systems with jointly connected topologies and nonlinear dynamics is considered, in which the nonlinear dynamics are assumed to be non-identical and unknown. The unknown nonlinear dynamics existing in the systems are assumed to be linearly parameterized, and an adaptive design method for leaderless multiagent systems is presented. By just using the relative position information between each agent and its neighbours, a distributed adaptive consensus control algorithm for the considered systems is proposed, in which the network graphs are jointly connected. Both the global uniform asymptotical stability and the global uniform asymptotical parameter convergence analysis of the adaptive control algorithm are carried out by using adaptive control theory, Lyapunov theory and algebraic graph theory. Finally, an example is given to illustrate the validity of our theoretical results.en_ZA
dc.description.departmentElectrical, Electronic and Computer Engineeringen_ZA
dc.description.embargo2018-06-30
dc.description.librarianhb2017en_ZA
dc.description.sponsorshipThe National Natural Science Foundation (NNSF) of China (61273183, 61374028 and 61304162).en_ZA
dc.description.urihttp://www.elsevier.com/locate/neucomen_ZA
dc.identifier.citationYu, H & Xia, XH 2017, 'Adaptive leaderless consensus of agents in jointly connected networks', Neurocomputing, vol. 241, pp. 64-70.en_ZA
dc.identifier.issn0925-2312 (print)
dc.identifier.issn1872-8286 (online)
dc.identifier.other10.1016/j.neucom.2017.02.031
dc.identifier.urihttp://hdl.handle.net/2263/60342
dc.language.isoenen_ZA
dc.publisherElsevieren_ZA
dc.rights© 2017 Elsevier B.V. All rights reserved. Notice : this is the author’s version of a work that was accepted for publication in Neurocomputing. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. A definitive version was subsequently published in Neurocomputing, vol. 241, pp. 64-70, 2017. doi : 10.1016/j.neucom.2017.02.031.en_ZA
dc.subjectAdaptive consensusen_ZA
dc.subjectDecentralized controlen_ZA
dc.subjectParameter convergenceen_ZA
dc.subjectJointly connected topologyen_ZA
dc.subjectMulti-agent systemen_ZA
dc.titleAdaptive leaderless consensus of agents in jointly connected networksen_ZA
dc.typePostprint Articleen_ZA

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