Meng Xueyang, Wang Zidong, Wang Fan, Chen Yun
IEEE Trans Neural Netw Learn Syst. 2024 Dec;35(12):17493-17503. doi: 10.1109/TNNLS.2023.3304515. Epub 2024 Dec 2.
This article is concerned with the state estimation problem for a class of complex networks (CNs) with uncertain inner couplings and packet losses over communication networks. The inner couplings are allowed to be uncertain and varying in a specific interval. The amplify-and-forward (AaF) relay protocols are introduced to improve the communication quality and enhance the propagation distance. The Bernoulli random variables are used to characterize the randomly occurring packet losses encountered in communication channels. The focus of this article is on the design of a state estimator for each node of CNs such that a prescribed performance constraint is satisfied for the dynamical error system over a finite horizon. A sufficient condition is first provided to verify the existence of the desired state estimator, and the estimator gain is then determined by solving two coupled backward Riccati difference equations (RDEs). Subsequently, a recursive state estimation algorithm is put forward that is suitable for online computation. Finally, a numerical example is given to demonstrate the effectiveness of the proposed estimation method.
本文关注一类具有不确定内部耦合且通信网络存在数据包丢失的复杂网络(CNs)的状态估计问题。内部耦合允许不确定且在特定区间内变化。引入放大转发(AaF)中继协议以提高通信质量并增加传播距离。使用伯努利随机变量来表征通信信道中随机出现的数据包丢失。本文的重点在于为复杂网络的每个节点设计一个状态估计器,使得在有限时间范围内动态误差系统满足规定的性能约束。首先提供一个充分条件来验证所需状态估计器的存在性,然后通过求解两个耦合的反向黎卡提差分方程(RDEs)确定估计器增益。随后,提出一种适用于在线计算的递归状态估计算法。最后,给出一个数值例子以证明所提估计方法的有效性。