Adaptive synchronization control for stochastic complex networks with derivative coupling

IF 3.2 Q2 AUTOMATION & CONTROL SYSTEMS
Yujing Shi, Lulu Yao, Shanqiang Li
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引用次数: 0

Abstract

In this paper, the problem of the adaptive synchronization control is studied for a class of stochastic complex networks with unknown nonlinear coupling strength and derivative coupling. First, in order to deal with the unknown nonlinear coupling strength, Takagi–Sugeno (T–S) fuzzy method is used to transform the network model into a T–S fuzzy complex network model. Then,a fuzzy adaptive controller and the corresponding adaptive parameter update rate are designed. Subsequently, a new Lyapunov function is constructed, which is related to the derivative coupling. By employing the stochastic analysis technique and Lyapunov stability theory, a sufficient condition is given for exponential stabilization in mean square of the synchronization error system. Finally, the effectiveness of the obtained theoretical results is verified through a simulation.
具有导数耦合的随机复杂网络的自适应同步控制
研究了一类非线性耦合强度和导数耦合未知的随机复杂网络的自适应同步控制问题。首先,为了处理未知的非线性耦合强度,采用Takagi-Sugeno (T-S)模糊方法将网络模型转化为T-S模糊复杂网络模型。然后,设计了模糊自适应控制器和相应的自适应参数更新速率。随后,构造了一个新的Lyapunov函数,该函数与导数耦合有关。利用随机分析技术和李雅普诺夫稳定性理论,给出了同步误差系统均方指数稳定的充分条件。最后,通过仿真验证了所得理论结果的有效性。
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来源期刊
Systems Science & Control Engineering
Systems Science & Control Engineering AUTOMATION & CONTROL SYSTEMS-
CiteScore
9.50
自引率
2.40%
发文量
70
审稿时长
29 weeks
期刊介绍: Systems Science & Control Engineering is a world-leading fully open access journal covering all areas of theoretical and applied systems science and control engineering. The journal encourages the submission of original articles, reviews and short communications in areas including, but not limited to: · artificial intelligence · complex systems · complex networks · control theory · control applications · cybernetics · dynamical systems theory · operations research · systems biology · systems dynamics · systems ecology · systems engineering · systems psychology · systems theory
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