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Event-triggered communication for synchronization of Markovian jump delayed complex networks with partially unknown transition rates

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  • Zhou, Jiamu
  • Dong, Hailing
  • Feng, Jianwen

Abstract

In this paper, exponential synchronization problems are investigated for an array of Markovian jump delayed complex networks with partially unknown transition rates and discontinuous diffusions. To impel the array complex networks to achieve exponential synchronization, a new randomly occurring event-triggered control strategy is proposed. The idea of event-triggered control strategy is that the coupling term and controller update data only at the event-triggered instants, which can reduce the communication load and energy consumption. By constructing a novel stochastic Lyapunov–Krasovskii function, some exponential synchronization criteria are obtained in terms of LMIs and famous Halanay inequality. Furthermore, we obtain a positive lower bound of the event intervals which can exclude the Zeno behaviors. Finally, a simulation example is given to illustrate the effectiveness of the theoretical results.

Suggested Citation

  • Zhou, Jiamu & Dong, Hailing & Feng, Jianwen, 2017. "Event-triggered communication for synchronization of Markovian jump delayed complex networks with partially unknown transition rates," Applied Mathematics and Computation, Elsevier, vol. 293(C), pages 617-629.
    • Handle: RePEc:eee:apmaco:v:293:y:2017:i:c:p:617-629
    DOI: 10.1016/j.amc.2016.06.039
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    Cited by:

    1. Pradeep, C. & Cao, Yang & Murugesu, R. & Rakkiyappan, R., 2019. "An event-triggered synchronization of semi-Markov jump neural networks with time-varying delays based on generalized free-weighting-matrix approach," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 155(C), pages 41-56.
    2. Yao, Xiuming & Lian, Yue & Park, Ju H., 2019. "Disturbance-observer-based event-triggered control for semi-Markovian jump nonlinear systems," Applied Mathematics and Computation, Elsevier, vol. 363(C), pages 1-1.
    3. Liu, Shouqiang & Yu, Mengjing & Li, Miao & Xu, Qingzhen, 2019. "The research of virtual face based on Deep Convolutional Generative Adversarial Networks using TensorFlow," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 521(C), pages 667-680.
    4. Li, Qiaoping & Liu, Sanyang & Chen, Yonggang, 2018. "Combination event-triggered adaptive networked synchronization communication for nonlinear uncertain fractional-order chaotic systems," Applied Mathematics and Computation, Elsevier, vol. 333(C), pages 521-535.
    5. Liang, Kun & Dai, Mingcheng & Shen, Hao & Wang, Jing & Wang, Zhen & Chen, Bo, 2018. "L2−L∞ synchronization for singularly perturbed complex networks with semi-Markov jump topology," Applied Mathematics and Computation, Elsevier, vol. 321(C), pages 450-462.
    6. Wu, Tao & Cao, Jinde & Xiong, Lianglin & Zhang, Haiyang & Shu, Jinlong, 2022. "Sampled-data synchronization criteria for Markovian jumping neural networks with additive time-varying delays using new techniques," Applied Mathematics and Computation, Elsevier, vol. 413(C).
    7. Zhen Zhou & Hongbin Wang & Zhongquan Hu, 2018. "Event-Based Time Varying Formation Control for Multiple Quadrotor UAVs with Markovian Switching Topologies," Complexity, Hindawi, vol. 2018, pages 1-15, April.
    8. Zhang, Ruimei & Zeng, Deqiang & Zhong, Shouming & Yu, Yongbin, 2017. "Event-triggered sampling control for stability and stabilization of memristive neural networks with communication delays," Applied Mathematics and Computation, Elsevier, vol. 310(C), pages 57-74.
    9. Fei Wang & Zhaowen Zheng & Yongqing Yang, 2019. "Synchronization of Complex Dynamical Networks with Hybrid Time Delay under Event-Triggered Control: The Threshold Function Method," Complexity, Hindawi, vol. 2019, pages 1-17, December.

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