Li Qi, Wang Zidong, Li Nan, Sheng Weiguo
IEEE Trans Neural Netw Learn Syst. 2020 Oct;31(10):4381-4388. doi: 10.1109/TNNLS.2019.2951948. Epub 2019 Dec 11.
This article deals with the recursive filtering issue for a class of nonlinear complex networks (CNs) with switching topologies, random sensor failures and dynamic event-triggered mechanisms. A Markov chain is utilized to characterize the switching behavior of the network topology. The phenomenon of sensor failures occurs in a random way governed by a set of stochastic variables obeying certain probability distributions. In order to save communication cost, a dynamic event-triggered transmission protocol is introduced into the transmission channel from the sensors to the recursive filters. The objective of the addressed problem is to design a set of dynamic event-triggered filters for the underlying CN with a certain guaranteed upper bound (on the filtering error covariance) that is then locally minimized. By employing the induction method, an upper bound is first obtained on the filtering error covariance and subsequently minimized by properly designing the filter parameters. Finally, a simulation example is provided to demonstrate the effectiveness of the proposed filtering scheme.
本文研究了一类具有切换拓扑、随机传感器故障和动态事件触发机制的非线性复杂网络（CNs）的递归滤波问题。利用马尔可夫链来表征网络拓扑的切换行为。传感器故障现象以随机方式发生，由一组服从特定概率分布的随机变量控制。为了节省通信成本，将动态事件触发传输协议引入从传感器到递归滤波器的传输通道。所解决问题的目标是为底层复杂网络设计一组动态事件触发滤波器，并具有一定的保证上界（关于滤波误差协方差），然后在局部将其最小化。通过采用归纳法，首先获得滤波误差协方差的上界，随后通过适当设计滤波器参数将其最小化。最后，提供了一个仿真示例来证明所提出滤波方案的有效性。
