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Stochastic Sampled-Data Control for Exponential Synchronization of Markovian Jumping Complex Dynamical Networks with Mode-Dependent Time-Varying Coupling Delay

机译:具有模式相关时变耦合时滞的马尔可夫跳跃复杂动态网络指数同步的随机采样数据控制

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摘要

In this paper, we address the problem of exponential synchronization of Markovian jumping complex dynamical networks with mode-dependent time-varying coupling delay via a stochastic sampled-data controller. In addition, the sampling period is assumed to be time-varying and switches between (m) different values in a random way with given probability. By constructing a new Lyapunov–Krasovskii functional (LKF) with triple integral terms and by employing convex combination technique and free weighting matrices method, sufficient conditions for the coupled complex dynamical network to be globally exponentially synchronized in the mean square sense are derived. The information about the lower bound of the discrete time-varying delay is used in the LKF. Based on the derived condition, the desired sampled-data feedback controller is designed in terms of the solution to linear matrix inequalities. Finally, two numerical examples are given to illustrate the effectiveness of the proposed methods.
机译:在本文中,我们通过随机采样数据控制器解决了具有模式相关时变耦合时滞的马尔可夫跳跃复杂动力学网络的指数同步问题。另外,假设采样周期是随时间变化的,并且以给定的概率以随机方式在(m)个不同值之间切换。通过构造具有三重积分项的新的Lyapunov-Krasovskii泛函(LKF),并采用凸组合技术和自由加权矩阵方法,可以为耦合复杂动力学网络在均方意义上全局指数同步提供充分的条件。在LKF中使用有关离散时变延迟下限的信息。基于导出的条件,根据线性矩阵不等式的解来设计所需的采样数据反馈控制器。最后,给出了两个数值例子来说明所提方法的有效性。

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