文献类型：期刊文献

英文题名：Adaptive Event-Triggered Transmission Scheme and H∞ Filtering Co-Design Over a Filtering Network With Switching Topology

作者：Zhang, Hao[1];Wang, Zhuping[1];Yan, Huaicheng[2,3];Yang, Fuwen[4];Zhou, Xue[1]

机构：[1]Tongji Univ, Dept Control Sci & Engn, Shanghai 200092, Peoples R China;[2]East China Univ Sci & Technol, Minist Educ, Key Lab Adv Control & Optimizat Chem Proc, Shanghai 200237, Peoples R China;[3]Hubei Normal Univ, Coll Mechatron & Control Engn, Huangshi 435002, Hubei, Peoples R China;[4]Griffith Univ, Griffith Sch Engn, Gold Coast, Qld 4222, Australia

年份：2019

卷号：49

期号：12

起止页码：4296

外文期刊名：IEEE TRANSACTIONS ON CYBERNETICS

收录：;EI(收录号：20183805820319);WOS:【SCI-EXPANDED(收录号:WOS:000485687200021)】；

基金：This work was supported in part by the National Natural Science Foundation of China under Grant 61773289, Grant 61673178, and Grant u1764261, in part by the Shanghai International Science and Technology Cooperation Project under Grant 18510711100 and Grant 15220710700, in part by the Shanghai Natural Science Foundation under Grant 17ZR1445800 and Grant 17ZR1444700, in part by the Shanghai Shuguang Project under Grant 16SG28, and in part by the Fundamental Research Funds for the Central Universities.

语种：英文

外文关键词：Distributed H-infinity filtering; event-triggered strategy; nonhomogeneous Markov chain; switching topology

摘要：This paper addresses the distributed adaptive event-triggered H-infinity filtering problem for a class of sector-bounded nonlinear system over a filtering network with time-varying and switching topology. Both topology switching and adaptive event-triggered mechanisms (AETMs) between filters are simultaneously considered in the filtering network design. The communication topology evolves over time, which is assumed to be subject to a nonhomogeneous Markov chain. In consideration of the limited network bandwidth, AETMs have been used in the information transmission from the sensor to the filter as well as the information exchange among filters. The proposed AETM is characterized by introducing the dynamic threshold parameter, which provides benefits in data scheduling. Moreover, the gain of the correction term in the adaptive rule varies directly with the estimation error and inversely with the transmission error. The switching filtering network is modeled by a Markov jump nonlinear system. The stochastic Markov stability theory and linear matrix inequality techniques are exploited to establish the existence of the filtering network and further derive the filter parameters. A co-design algorithm for determining H-infinity filters and the event parameters is developed. Finally, some simulation results on a continuous stirred tank reactor and a numerical example are presented to show the applicability of the obtained results.