文献类型：期刊文献

英文题名：Neural-Network-Based Set-Membership Filtering Under WTOD Protocols via a Novel Event-Triggered Compensation Mechanism

作者：Yang, Hao[1];Yan, Huaicheng[1,2];Zhou, Jing[3];Zhang, Yilian[4];Chang, Yufang[2]

机构：[1]East China Univ Sci & Technol, Key Lab Smart Mfg Energy Chem Proc, Minist Educ, Shanghai 200237, Peoples R China;[2]Hubei Univ Technol, Hubei Key Lab High Efficiency Utilizat Solar Energ, Wuhan 430068, Peoples R China;[3]Jianghan Univ, Sch Artificial Intelligence, Wuhan 430068, Peoples R China;[4]Shanghai Maritime Univ, Key Lab Transport Ind Marine Technol & Control Eng, Shanghai 201306, Peoples R China

年份：2024

卷号：54

期号：5

起止页码：2954

外文期刊名：IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS

收录：;EI(收录号：20240615527205);WOS:【SCI-EXPANDED(收录号:WOS:001167306700001)】；

基金：No Statement Available

语种：英文

外文关键词：Event-triggered compensation mechanism; neural-networks (NNs); set-membership filtering; weighted try-once-discard (WTOD) protocol

摘要：This article investigates the neural-network-based (NN-based) set-membership filtering issue for nonlinear systems. In order to lighten the network transmission burden and avoid data collisions, the weighted try-once-discard (WTOD) protocol is employed to regulate the signal transmission process, which provides higher transmission priority to the most needed data. Considering the data discarding problem of the WTOD protocol, a novel event-triggered compensation mechanism is proposed to compensate the measurement output processed by the WTOD protocol, thereby improving the filtering performance. Next, considering the nonlinear dynamics of the system and the unknown-but-bounded (UBB) noise interference, an NN-based set-membership filter is designed to solve the state estimation problem. In a unified set-membership framework, an neural-network (NN) weight adaptive tuning law and a state estimation algorithm are designed. Sufficient conditions are derived for the existence of the adaptive NN parameters and the NN-based set-membership filter, and two optimization problems are put forward to seek the optimal NN parameters and filtering parameters that make the filter performance optimal. Finally, illustrative examples demonstrate the effectiveness of the proposed compensation mechanism and filtering algorithm.