Robust State Estimation for State-Saturated Complex Networks with Communication Protocol Schedule

Chaoqing Jia, Jun Hu, Na Lin, Jinpeng Huang, Zhipeng Cao
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Abstract

In this paper, we discuss the variance- constrained robust state estimation problem for a class of state-saturated complex networks (CNs) subject to randomly occurring uncertainties (ROUs), stochastic coupling and Random Access Protocol (RAP). The RAP as a scheduling mode is employed to regulate the transmission sequences among the network nodes. The ROUs and stochastic coupling are both described by Bernoulli random variables with uncertain occurrence probabilities. This paper attempts to provide a variance-constrained robust state estimation method for CNs such that, for all ROUs, stochastic coupling as well as RAP, an upper bound of estimation error covariance is derived by means of designing the estimator gain in a proper way. Furthermore, a sufficient condition is established to ensure that the trace of such upper bound is uniformly bounded. Finally, a numerical simulation is provided to verify the effectiveness of the presented variance-constrained robust state estimation strategy.
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具有通信协议调度的状态饱和复杂网络的鲁棒状态估计
本文讨论了一类具有随机不确定性、随机耦合和随机接入协议的状态饱和复杂网络的方差约束鲁棒状态估计问题。RAP作为一种调度模式,用于调节网络节点间的传输顺序。ROUs和随机耦合都是由具有不确定发生概率的伯努利随机变量来描述的。本文试图提供一种方差约束的神经网络鲁棒状态估计方法,通过合理设计估计器增益,对所有的ROUs、随机耦合和RAP,都推导出估计误差协方差的上界。进一步给出了该上界轨迹一致有界的充分条件。最后,通过数值仿真验证了方差约束鲁棒状态估计策略的有效性。
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