文献类型：期刊文献

英文题名：Adaptive Neural Network Output-Feedback Control for Uncertain Nonlinear Systems via Event-Triggered Output

作者：Hu, Yunsong[1];Yan, Huaicheng[1,2];Zhang, Hao[3];Wang, Meng[1];Chen, Chaoyang[2]

机构：[1]East China Univ Sci & Technol, Key Lab Smart Mfg Energy Chem Proc, Minist Educ, Shanghai 200237, Peoples R China;[2]Hunan Univ Sci & Technol, Sch Informat & Elect Engn, Xiangtan 411201, Peoples R China;[3]Tongji Univ, Dept Control Sci & Engn, Shanghai 200092, Peoples R China

年份：2024

卷号：54

期号：10

起止页码：5864

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

收录：;EI(收录号：20243917098248);WOS:【SCI-EXPANDED(收录号:WOS:001272998800001)】；

基金：This work was supported in part by the National Natural Science Foundation of China under Grant 62333005,Grant 62073143, and Grant 62373152; and in part by the Innovation Program of Shanghai Municipal Education Commission under Grant 2021-01-07-00-02-E00105.

语种：英文

外文关键词：Adaptive control; backstepping control; event-triggered control (ETC); neural network (NN); nonlinear systems; output feedback; Adaptive control; backstepping control; event-triggered control (ETC); neural network (NN); nonlinear systems; output feedback

摘要：This article systematically studies the issue of adaptive neural network (NN) output-feedback control for uncertain nonlinear systems using event-triggered output. First, to tackle the problem of unmeasurable states, a compact state observer using event-triggered output is constructed. Then, since the event-triggered output signals are discontinuous, the virtual control laws in backstepping design are no longer differentiable. Hence, the dynamic surface control scheme is introduced to resolve this problem. Unlike existing work requiring system functions to satisfy Lipschitz continuity condition, adaptive NN control is incorporated into the designed algorithm to relax the above constraint. What is more, the event-triggered mechanism is also used for parameter estimation to avoid waste of computing and communication resources. Finally, the results of comparative simulations and the DC brush motor experiment are depicted to demonstrate the practicality and effectiveness of the proposed method.