Li Bing, Wang Zidong, Ma Lifeng
IEEE Trans Neural Netw Learn Syst. 2018 Dec;29(12):5812-5822. doi: 10.1109/TNNLS.2018.2812098. Epub 2018 Mar 29.
This paper is concerned with the synchronization analysis and control problems for a class of nonlinear discrete-time stochastic complex dynamical networks (CDNs) consisting of identical nodes. The discrete-time stochastic dynamical networks under consideration are quite general that account for asymmetric coupling configuration, nonlinear inner coupling structures as well as nonidentical exogenous disturbances. By resorting to both the error bound and the synchronization probability, a notion of quasi-synchronization in probability is first introduced to assess the synchronization performance of the addressed CDNs. An event-triggered pinning feedback control strategy is adopted to control a small fraction of the network nodes with hope to reduce the frequency of updating and communication in the control process while preserving the desired dynamical behaviors of the controlled networks. By using the Lyapunov function method and the stochastic analysis techniques, a general framework is established within which the problems of dynamics analysis and controller synthesis are solved for the closed-loop stochastic dynamical networks. Two numerical examples and their simulations are presented to illustrate the effectiveness and the usefulness of our theoretical results.
本文关注一类由相同节点组成的非线性离散时间随机复杂动力网络（CDN）的同步分析与控制问题。所考虑的离散时间随机动力网络非常一般，它考虑了不对称耦合配置、非线性内部耦合结构以及不相同的外部干扰。通过借助误差界和同步概率，首先引入概率准同步的概念来评估所研究的CDN的同步性能。采用事件触发的牵制反馈控制策略来控制一小部分网络节点，以期在保持受控网络期望动态行为的同时，降低控制过程中的更新和通信频率。通过使用李雅普诺夫函数方法和随机分析技术，建立了一个通用框架，在该框架内解决了闭环随机动力网络的动力学分析和控制器综合问题。给出了两个数值例子及其仿真结果，以说明我们理论结果的有效性和实用性。
