Collective surrounding control in multi-agent networks

doi:10.1088/1674-1056/23/5/050201

Abstract We study the collective encirclement control problem of a multi-agent system in this paper, where the agents move collectively to encircle the multiple targets. An algorithm is proposed and a sufficient condition is derived to realize the collective encirclement motion. Some simulation results are provided to show the effectiveness of the obtained theoretical results.

Keywords: surrounding motion networks multiple targets

Received: 09 June 2013
Revised: 21 October 2013
Accepted manuscript online:

PACS:	02.10.Yn	(Matrix theory)
	05.65.+b	(Self-organized systems)
	87.10.-e	(General theory and mathematical aspects)

Fund: Project supported by the National Natural Science Foundation of China (Grant No. 61203080), the Foundation of State Key Laboratory of Networking and Switching Technology Foundation, China (Grant No. SKLNST2011105), and the Fundamental Research Funds for the Central Universities, China (Grant Nos. ZYGX2010J113 and ZYGX2010J114).

Corresponding Authors: Wei Ting-Ting
E-mail: weiwtt@163.com

About author: 02.10.Yn; 05.65.+b; 87.10.-e

Cite this article:

Wei Ting-Ting (魏婷婷), Chen Xiao-Ping (陈小平) Collective surrounding control in multi-agent networks 2014 Chin. Phys. B 23 050201

[1]	Lin P, Jia Y and Li L 2008 Systems and Control Letters 643 653
[2]	Lin P and Jia Y 2008 Physica A 387 303
[3]	Choi J, Oh S and Horowitz R 2009 Automatica 2802 2814
[4]	Hong Y, Gao L, Cheng D and Hu J 2007 IEEE Trans. on Automatic Control 943 948
[5]	Hong Y, Chen G and Bushnell L 2008 Automatica 846 850
[6]	Guan Z, Wu Y and Feng G 2012 IEEE Trans. on Circuits and Systems 170 178
[7]	Liu Z, Guan Z and Shen X 2012 IEEE Trans. on Automatica Control 2639 2643
[8]	Guan Z, Liu Z and Feng G 2012 Automatica 1397 1404
[9]	Lin P and Jia Y 2009 Automatica 45 2154
[10]	Lin P and Jia Y 2010 IEEE Trans. Automatic Control 55 778
[11]	Lin P and Jia Y 2011 Automatica 47 848
[12]	Zhang W, Liu J, Zeng D and Hu Y 2013 Chin. Phys. B 22 050511
[13]	Marasco A J, Givigi S N and Rabbath C A 2012 American Control Conference 2004 2009
[14]	Chen F, Ren W and Cao Y 2010 Systems and Control Letters 704 712
[15]	Hu J and Yuan H 2009 Chin. Phys. B 18 3777
[16]	Guan X and Li Y 2009 Chin. Phys. B 18 3355
[17]	Pei W, Chen Z and Yuan Z 2008 Chin. Phys. B 17 337
[18]	Lin P and Jia Y 2008 Physica A 387 303
[19]	Godsil C and Royle G 2001 Algebraic Graph Theory (New York: Springer-Verlag)

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