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有限时域上离散时变随机复杂网络的有界H∞同步与状态估计

Bounded H∞ synchronization and state estimation for discrete time-varying stochastic complex networks over a finite horizon.

作者信息

Shen Bo, Wang Zidong, Liu Xiaohui

机构信息

School of Information Science and Technology, Donghua University, Shanghai 200051, China.

出版信息

IEEE Trans Neural Netw. 2011 Jan;22(1):145-57. doi: 10.1109/TNN.2010.2090669. Epub 2010 Nov 18.

Abstract

In this paper, new synchronization and state estimation problems are considered for an array of coupled discrete time-varying stochastic complex networks over a finite horizon. A novel concept of bounded H(∞) synchronization is proposed to handle the time-varying nature of the complex networks. Such a concept captures the transient behavior of the time-varying complex network over a finite horizon, where the degree of bounded synchronization is quantified in terms of the H(∞)-norm. A general sector-like nonlinear function is employed to describe the nonlinearities existing in the network. By utilizing a time-varying real-valued function and the Kronecker product, criteria are established that ensure the bounded H(∞) synchronization in terms of a set of recursive linear matrix inequalities (RLMIs), where the RLMIs can be computed recursively by employing available MATLAB toolboxes. The bounded H(∞) state estimation problem is then studied for the same complex network, where the purpose is to design a state estimator to estimate the network states through available output measurements such that, over a finite horizon, the dynamics of the estimation error is guaranteed to be bounded with a given disturbance attenuation level. Again, an RLMI approach is developed for the state estimation problem. Finally, two simulation examples are exploited to show the effectiveness of the results derived in this paper.

摘要

本文研究了有限时间范围内耦合离散时变随机复杂网络阵列的新同步和状态估计问题。提出了一种有界H(∞)同步的新概念来处理复杂网络的时变特性。该概念捕捉了有限时间范围内时变复杂网络的瞬态行为,其中有界同步程度通过H(∞)范数来量化。采用一般的扇形非线性函数来描述网络中存在的非线性。通过利用时变实值函数和克罗内克积,建立了基于一组递归线性矩阵不等式(RLMI)确保有界H(∞)同步的准则,其中RLMI可通过使用现有的MATLAB工具箱进行递归计算。然后针对同一复杂网络研究了有界H(∞)状态估计问题,其目的是设计一个状态估计器,通过可用的输出测量来估计网络状态,使得在有限时间范围内,估计误差的动态特性在给定的干扰衰减水平下保证有界。同样,针对状态估计问题开发了一种RLMI方法。最后,通过两个仿真例子展示了本文所得结果的有效性。

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