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基于记忆采样数据控制的时滞复杂动态网络的非脆弱指数同步。

Nonfragile Exponential Synchronization of Delayed Complex Dynamical Networks With Memory Sampled-Data Control.

出版信息

IEEE Trans Neural Netw Learn Syst. 2018 Jan;29(1):118-128. doi: 10.1109/TNNLS.2016.2614709. Epub 2016 Oct 24.

Abstract

This paper considers nonfragile exponential synchronization for complex dynamical networks (CDNs) with time-varying coupling delay. The sampled-data feedback control, which is assumed to allow norm-bounded uncertainty and involves a constant signal transmission delay, is constructed for the first time in this paper. By constructing a suitable augmented Lyapunov function, and with the help of introduced integral inequalities and employing the convex combination technique, a sufficient condition is developed, such that the nonfragile exponential stability of the error system is guaranteed. As a result, for the case of sampled-data control free of norm-bound uncertainties, some sufficient conditions of sampled-data synchronization criteria for the CDNs with time-varying coupling delay are presented. As the formulations are in the framework of linear matrix inequality, these conditions can be easily solved and implemented. Two illustrative examples are presented to demonstrate the effectiveness and merits of the proposed feedback control.

摘要

本文考虑了具有时变耦合延迟的复杂动力网络(CDN)的非脆弱指数同步。本文首次为 CDN 构建了具有范数有界不确定性的采样数据反馈控制,并且涉及常数信号传输延迟。通过构造合适的增广 Lyapunov 函数,并借助引入的积分不等式和使用凸组合技术,提出了一个充分条件,以保证误差系统的非脆弱指数稳定性。因此,对于没有范数不确定性的采样数据控制情况,给出了具有时变耦合延迟的 CDN 的采样数据同步判据的一些充分条件。由于这些公式是在线性矩阵不等式的框架内,因此这些条件可以很容易地解决和实现。通过两个说明性示例,验证了所提出反馈控制的有效性和优点。

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