Randomly Occurring Cluster Synchronization of Complex Networks via Adaptive Pinning Control

2021 IEEE 10th Data Driven Control and Learning Systems Conference (DDCLS) Pub Date : 2021-05-14 DOI:10.1109/DDCLS52934.2021.9455459

Chenhui Jiang, Dong Ding, Jiancheng Zhang, Ze Tang

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Abstract

The article studies the cluster synchronization for a kind of nonlinear coupled complex network with time-varying delay. Considering the networks may subject to certain uncertainties, the model of complex networks consisting of nonidentical systems with randomly occurring disturbance which described by Bernoulli stochastic variable is established. Secondly, a kind of pinning feedback controllers under randomly occurring disturbance is proposed in order to not only synchronize the systems in the same clusters but also weaken the mutual influence among clusters, which will be imposed on the systems in current cluster which have directed connections with the systems in the other clusters. Then, sufficient conditions for the realization of the cluster synchronization are derived in terms of the QUAD function class, the NCF function class and the Lyapunov stability theorem. Furthermore, the optimal feedback control gain is obtained by designing the adaptive updating laws. Finally, a numerical experiment is presented to illustrate the effectiveness of theoretical analysis.

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基于自适应固定控制的复杂网络随机集群同步

研究一类具有时变时滞的非线性耦合复杂网络的集群同步问题。考虑到网络可能存在一定的不确定性，建立了用伯努利随机变量描述的具有随机扰动的非同系统组成的复杂网络模型。其次，提出了一种随机扰动下的钉住反馈控制器，既能使同一集群内的系统同步，又能减弱集群间的相互影响，这种影响将施加在当前集群中与其他集群中系统有直接连接的系统上。然后，利用QUAD函数类、NCF函数类和Lyapunov稳定性定理，推导了实现集群同步的充分条件。通过设计自适应更新律，获得了最优反馈控制增益。最后，通过数值实验验证了理论分析的有效性。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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2021 IEEE 10th Data Driven Control and Learning Systems Conference (DDCLS)

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