DocumentCode :
9777
Title :
Second-Order Global Consensus in Multiagent Networks With Random Directional Link Failure
Author :
Huaqing Li ; Xiaofeng Liao ; Tingwen Huang ; Wei Zhu ; Yanbing Liu
Author_Institution :
Coll. of Electron. & Inf. Eng., Southwest Univ., Chongqing, China
Volume :
26
Issue :
3
fYear :
2015
fDate :
Mar-15
Firstpage :
565
Lastpage :
575
Abstract :
In this paper, we consider the second-order globally nonlinear consensus in a multiagent network with general directed topology and random interconnection failure by characterizing the behavior of stochastic dynamical system with the corresponding time-averaged system. A criterion for the second-order consensus is derived by constructing a Lyapunov function for the time-averaged network. By associating the solution of random switching nonlinear system with the constructed Lyapunov function, a sufficient condition for second-order globally nonlinear consensus in a multiagent network with random directed interconnections is also established. It is required that the second-order consensus can be achieved in the time-averaged network and the Lyapunov function decreases along the solution of the random switching nonlinear system at an infinite subsequence of the switching moments. A numerical example is presented to justify the correctness of the theoretical results.
Keywords :
Lyapunov methods; multi-agent systems; multi-robot systems; network theory (graphs); nonlinear control systems; stochastic systems; topology; Lyapunov function; directed topology; multiagent networks; random directed interconnection; random directional link failure; random switching nonlinear system; second-order globally nonlinear consensus; stochastic dynamical system; time-averaged network; time-averaged system; Laplace equations; Lyapunov methods; Multi-agent systems; Network topology; Nonlinear dynamical systems; Switches; Topology; Global consensus; multiagent network; nonlinear dynamics; random switching; second-order consensus; second-order consensus.;
fLanguage :
English
Journal_Title :
Neural Networks and Learning Systems, IEEE Transactions on
Publisher :
ieee
ISSN :
2162-237X
Type :
jour
DOI :
10.1109/TNNLS.2014.2320274
Filename :
6817568
Link To Document :
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