Yu, HuiXia, Xiaohua2017-05-122017-06Yu, H & Xia, XH 2017, 'Adaptive leaderless consensus of agents in jointly connected networks', Neurocomputing, vol. 241, pp. 64-70.0925-2312 (print)1872-8286 (online)10.1016/j.neucom.2017.02.031http://hdl.handle.net/2263/60342In 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© 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.Adaptive consensusDecentralized controlParameter convergenceJointly connected topologyMulti-agent systemPostprint Article