Search

This article systematically studies consensus of linear multiagent systems (MASs) on directed graphs through adaptive event-triggered control. It presents innovative adaptive event-triggered state-feedback protocols with novel composite event-triggering conditions. Two specific designs in terms of different event-triggering conditions and laws of adaption are first discussed for linear MASs on strongly connected directed graphs, which are then extended to general directed graphs that contain a spanning tree. Moreover, another adaptive event-triggered protocol is proposed for solving leader–follower consensus that tracks a leader of a bounded control input. The protocols inherit the merits of both adaptive control and event-triggered control: the protocols can be implemented in a fully distributed way, since the Laplacian is avoided in design, and each agent only needs to know the relative information between neighbors at discrete instants determined by event-triggering conditions. Compared with the existing related results, the proposed protocols are applicable for linear MASs on general directed graphs, and moreover, the time-dependent term in the event-triggering conditions is allowed to be a class of positive L1 functions. Two numerical examples clearly verify the effectiveness of the proposed protocols. [ABSTRACT FROM AUTHOR]

This paper proposes distributed event-triggered schemes for achieving state consensus for multi-agent linear systems. For each agent modeled by a linear control system in R n , a positive signal is embedded in its event function, with the aim of guaranteeing an asymptotic convergence to state consensus for networked linear systems interacted in an undirected and connected graph, and with Zeno triggering excluded for all the agents. The proposed distributed event-based consensus algorithm allows each agent to update its own control at its own triggering times instead of using continuous updates, which thereby avoids complicated computation steps involving data fusion and matrix exponential calculations as used in several event-based control schemes reported in the literature. We further propose a totally distributed and adaptive event-based algorithm, in the sense that each agent utilizes only local measurements with respect to its neighboring agents in its event detection and control update. In this framework, the proposed algorithm is independent of any global network information such as Laplacian matrix eigenvalues associated with the underlying interaction graph. A positive L 1 signal function is included in the adaptive event-based algorithm to guarantee asymptotic consensus convergence and Zeno-free triggering for all the agents. Simulations are provided to validate the performance and superiority of the developed event-based consensus strategies. [ABSTRACT FROM AUTHOR]

This paper discusses adaptive synchronization control for complex networks interacted in an undirected weighted graph, and aims to provide a novel and general approach for the design of distributed update laws for adaptively adjusting coupling weights. The proposed updating laws are very general in the sense that they encompass most weight update laws reported in the literature as special cases, and also provide new insights in the analysis of network system evolution and graph weight convergence. We show a rigorous proof for the synchronization stability of the overall complex network to a synchronized state, and demonstrate the convergence of adaptive weights for each edge to some bounded constants. A detailed comparison with available results is provided to elaborate the new features and advantages of the proposed adaptive strategies as compared with conventional adaptive laws. The effectiveness of the proposed approach is also validated by several typical simulations. [ABSTRACT FROM AUTHOR]

Abstract: In this comment letter we point out that the main result of the recent paper [Xu Y, Zhou W, Fang J, Sun W. Topology identification and adaptive synchronization of uncertain complex networks with adaptive double scaling functions. Commun Nonlinear Sci Numer Simul 2011;16(18):3337–43] has certain errors. The mistakes are corrected and a correct version is presented in this letter. We further indicate that a sufficient condition has been neglected in a series of articles discussing the same topic of network topology identification; hence we hope this letter can help clarify some unclear concepts about this topic. [Copyright &y& Elsevier]