Globally Adaptive Neural Network Output-Feedback Control for Uncertain Nonlinear Systems

被引：10
作者：
Zhang, Zhengqiang [1 ]
Wang, Qiufeng [1 ]
Sang, Yingli [1 ]
Ge, Shuzhi Sam [2 ]
机构：
[1] Qufu Normal Univ, Sch Engn, Rizhao 276826, Peoples R China
[2] Natl Univ Singapore, Dept Elect & Comp Engn, Singapore 117576, Singapore
来源：
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS | 2023年 / 34卷 / 11期
基金：
中国国家自然科学基金;
关键词：
Artificial neural networks; Output feedback; Switches; Nonlinear systems; Adaptive control; Backstepping; Upper bound; globally uniformity ultimate boundedness; neural network (NN); output feedback control; uncertain nonlinear system; DYNAMIC SURFACE CONTROL; ORDER CHAOTIC SYSTEMS; TRACKING CONTROL; SYNCHRONIZATION;
D O I：
10.1109/TNNLS.2022.3155635
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
In this article, a globally neural-network-based adaptive control strategy with flat-zone modification is proposed for a class of uncertain output feedback systems with time-varying bounded disturbances. A high-order continuously differentiable switching function is introduced into the filter dynamics to achieve global compensation for uncertain functions, thus further to ensure that all the closed-loop signals are globally uniformity ultimately bounded (GUUB). It is proven that the output tracking error converges to the prespecified neighborhood of the origin. The effectiveness of the proposed control method is verified by two simulation examples.
引用
收藏
页码：9078 / 9087
页数：10
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