Li Qi, Wang Zidong, Hu Jun, Sheng Weiguo
IEEE Trans Neural Netw Learn Syst. 2022 Oct;33(10):5441-5451. doi: 10.1109/TNNLS.2021.3070797. Epub 2022 Oct 5.
This article addresses the simultaneous state and unknown input estimation problem for a class of discrete time-varying complex networks (CNs) under redundant channels and dynamic event-triggered mechanisms (ETMs). The redundant channels, modeled by an array of mutually independent Bernoulli distributed stochastic variables, are exploited to enhance transmission reliability. For energy-saving purposes, a dynamic event-triggered transmission scheme is enforced to ensure that every sensor node sends its measurement to the corresponding estimator only when a certain condition holds. The primary objective of the investigation carried out is to construct a recursive estimator for both the state and the unknown input such that certain upper bounds on the estimation error covariances are first guaranteed and then minimized at each time instant in the presence of dynamic event-triggered strategies and redundant channels. By solving two series of recursive difference equations, the desired estimator gains are computed. Finally, an illustrative example is presented to show the usefulness of the developed estimator design method.
本文研究了一类离散时变复杂网络（CNs）在冗余信道和动态事件触发机制（ETMs）下的状态和未知输入同时估计问题。由相互独立的伯努利分布随机变量阵列建模的冗余信道被用于提高传输可靠性。为了节能，实施了一种动态事件触发传输方案，以确保每个传感器节点仅在特定条件成立时才将其测量值发送给相应的估计器。所开展研究的主要目标是构造一个用于状态和未知输入的递归估计器，使得在存在动态事件触发策略和冗余信道的情况下，首先保证估计误差协方差的某些上界，然后在每个时刻将其上界最小化。通过求解两组递归差分方程，计算出所需的估计器增益。最后，给出一个示例来说明所提出的估计器设计方法的有效性。
