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Chin. Phys. B, 2015, Vol. 24(9): 098902    DOI: 10.1088/1674-1056/24/9/098902
INTERDISCIPLINARY PHYSICS AND RELATED AREAS OF SCIENCE AND TECHNOLOGY Prev   Next  

Synchronization of Markovian jumping complex networks with event-triggered control

Shao Hao-Yu (邵浩宇), Hu Ai-Hua (胡爱花), Liu Dan (刘丹)
School of Science, Jiangnan University, Wuxi 214122, China
Abstract  This paper investigates event-triggered synchronization for complex networks with Markovian jumping parameters. Nonlinear dynamics with Markovian jumping parameters is considered for each node in a complex network. By utilizing the proposed event-triggered strategy, and based on the Lyapunov functional method and linear matrix inequality technology, some sufficient conditions for synchronization of complex networks are derived whether the transition rate matrix for the Markov process is completely known or not. Finally, a numerical example is presented to illustrate the effectiveness of the proposed theoretical results.
Keywords:  complex networks      synchronization      event-triggered control      Markovian jumping parameters  
Received:  09 January 2015      Revised:  02 April 2015      Accepted manuscript online: 
PACS:  89.75.Fb (Structures and organization in complex systems)  
  05.45.Xt (Synchronization; coupled oscillators)  
  02.30.Yy (Control theory)  
  02.50.Ga (Markov processes)  
Fund: Project supported by the National Natural Science Foundation of China (Grant No. 11202084).
Corresponding Authors:  Hu Ai-Hua     E-mail:  aihuahu@126.com

Cite this article: 

Shao Hao-Yu (邵浩宇), Hu Ai-Hua (胡爱花), Liu Dan (刘丹) Synchronization of Markovian jumping complex networks with event-triggered control 2015 Chin. Phys. B 24 098902

[1] Wang L M, Tang Y G, Chai Y Q and Wu F 2014 Chin. Phys. B 23 100501
[2] Cai G L, Jiang S Q, Cai S M and Tian L X 2014 Chin. Phys. B 23 120505
[3] Zhang J B, Ma Z J and Zhang G 2014 Chin. Phys. B 23 010507
[4] Wang J A, Nie R X and Sun Z Y 2014 Chin. Phys. B 23 050509
[5] Yao C G, Zhao Q and Yu J 2013 Phys. Lett. A 377 370
[6] Sang J Y, Yang J and Yue L J 2011 Chin. Phys. B 20 080507
[7] Hu A H and Xu Z Y 2011 Commun. Nonlinear Sci. Numer. Simul. 16 3237
[8] Grassi G 2012 Chin. Phys. B 21 050505
[9] Pourdehi S and Karimaghaee P 2012 Chaos 22 023145
[10] Wang S, Yu Y G and Wen G G 2014 Nonlinear Analysis: Hybrid systems 11 129
[11] Yu W W, Zheng W X, Chen G R, Ren W and Cao J D 2011 Automatica 47 1496
[12] Rakkiyappan R, Sakthivel N and Lakshmanan S 2014 Chin. Phys. B 23 020205
[13] Wang Y, Wu Q H and Wang Y Q 2012 Syst. Control Lett. 61 1145
[14] Yang S F, Cao J D and Lu J Q 2012 Chaos 22 043134
[15] Yang W, Wang X F and Shi H B 2013 Syst. Control Lett. 62 269
[16] Meng X Y and Chen T W 2013 Automatica 49 2125
[17] Chen X and Hao F 2012 IET. Control Theory Appl. 6 2493
[18] Trimpe S and D'Andrea R 2011 Proceedings of the 50th IEEE Conference on Decision and Control and European Control Conference, Orlando, December 12-15, 2011, p. 2361
[19] Guo G, Ding L and Han Q L 2014 Automatica 50 1489
[20] Zhang J H and Feng G 2014 Automatica 50 1852
[21] Qi B, Cui B T and Lou X Y 2014 Chin. Phys. B 23 110501
[22] Hu S and Yue D 2012 Signal Process 92 2029
[23] Luo R Z and He L M 2014 Chin. Phys. B 23 070506
[24] Liu K E, Xie G M, Ren W and Wang L 2013 Syst. Control Lett. 62 152
[25] Zhang H W and Lewis F L 2012 Automatica 48 1432
[26] Rakkiyappan R, Chandrasekar A, Rihan F A and Lakshmanan S 2014 Math. Biosci. 251 30
[27] Zhang H G and Wang Y C 2008 IEEE Trans. Neural Netw. 19 366
[28] Zhang H G, Dong M, Wang Y C and Sun N 2010 Neurocomputing 73 2689
[29] Zhang H G, Fu J, Ma T D and Tong S C 2009 Chin. Phys. B 18 3325
[30] Yao X, Wu L, Zheng W X and Wang C 2011 Int. J. Syst. Sci. 42 1219
[31] Zhang L and Boukas E 2009 Automatica 45 463
[32] Ma Q, Xu S and Zou Y 2011 Neurocomputing 74 3404
[33] Horn R A and Johnson C R 1985 Matrix Analysis (Cambridge: Cambridge University Press)
[34] Zhang X M and Han Q L 2013 Int. J. Robust Nonlinear 23 1419
[35] Li H J, Ming C, Shen S G and Wong W K 2014 J. Franklin I. 351 2582
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