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Chin. Phys. B, 2012, Vol. 21(10): 100507    DOI: 10.1088/1674-1056/21/10/100507
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Spatiotemporal chaos synchronization of an uncertain network based on sliding mode control

Lü Ling (吕翎), Yu Miao (于淼), Wei Lin-Ling (韦琳玲) , Zhang Meng(张檬), Li Yu-Shan (李雨珊)
College of Physics and Electronic Technology, Liaoning Normal University, Dalian 116029, China
Abstract  The sliding mode control method is used to study spatiotemporal chaos synchronization of an uncertain network. The method is extended from synchronization between two chaotic systems to the synchronization of complex network composed of N spatiotemporal chaotic systems. The sliding surface of the network and the control input are designed. Furthermore, the effectiveness of the method is analysed based on the stability theory. The Burgers equation with spatiotemporal chaos behavior is taken as an example to simulate the experiment. It is found that the synchronization performance of the network is very stable.
Keywords:  spatiotemporal chaos synchronization      complex network      sliding mode control      Lyapunov theorem  
Received:  22 February 2012      Revised:  01 March 2012      Accepted manuscript online: 
PACS:  05.45.Xt (Synchronization; coupled oscillators)  
  05.45.Pq (Numerical simulations of chaotic systems)  
Fund: Project supported by the Natural Science Foundation of Liaoning Province, China (Grant No. 20082147) and the Innovative Team Program of Liaoning Educational Committee, China (Grant No. 2008T108).
Corresponding Authors:  Lü Ling     E-mail:  luling1960@yahoo.com.cn

Cite this article: 

Lü Ling (吕翎), Yu Miao (于淼), Wei Lin-Ling (韦琳玲) , Zhang Meng(张檬), Li Yu-Shan (李雨珊) Spatiotemporal chaos synchronization of an uncertain network based on sliding mode control 2012 Chin. Phys. B 21 100507

[1] Winfree A T 1967 J. Theor. Biol. 16 15
[2] Watts D J 1998 Nature 393 440
[3] Barabási A L and Albert R 1999 Science 286 509
[4] Pecora L M and Carroll T L 1998 Phys. Rev. Lett. 80 2109
[5] Donetti L, Hurtado P I and Muñoz M A 2005 Phys. Rev. Lett. 95 188701
[6] Zhao M, Zhou T, Wang B H, Yan G, Yang H J and Bai W J 2006 Physica A 371 773
[7] Li D, Leyva I, Almendral J A, Sendiña-Nadal I, Buldú J M, Havlin S and Boccaletti S 2008 Phys. Rev. Lett. 101 168701
[8] Kouvaris N, Provata A and Kugiumtzis D 2010 Phys. Lett. A 374 507
[9] Yu W W, Chen G R and Lü J H 2009 Automatica 45 429
[10] La Rocca C E, Braunstein L A and Macri P A 2009 Phys. Rev. E 80 26111
[11] Song Q, Cao J D and Liu F 2010 Phys. Lett. A 374 544
[12] Cui B T and Lou X Y 2009 Chaos, Solitons and Fractals 39 288
[13] Jalan S and Amritkar R E 2003 Phys. Rev. Lett. 90 014101
[14] Moreno Y and Pacheco A F 2004 Europhys. Lett. 68 603
[15] Acebrón J A, Bonilla L L,Vicente C J P, Ritort F and Spigler R 2005 Rev. Mod. Phys. 77 137
[16] Lü L, Li G, Guo L, Meng L, Zou J R and Yang M 2010 Chin. Phys. B 19 080507
[17] Shang Y, Chen M Y and Kurths J 2009 Phys. Rev. E 80 027201
[18] Zhu Q Y and Ma Y W 2000 Comput. Mech. 17 379 (in Chinese)
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