Asymptotic Tracking Control for a Class of Pure-Feedback Nonlinear Systems

被引:2
|
作者
Wei, Jianli [1 ]
Liu, Zongcheng [2 ]
机构
[1] Northwestern Polytech Univ, Unmanned Syst Res Inst, Xian 710072, Peoples R China
[2] Air Force Engn Univ, Aeronaut Engn Coll, Xian 710038, Peoples R China
基金
中国国家自然科学基金;
关键词
Asymptotic stability; neural networks; nonlinear control systems; pure-feedback systems; ADAPTIVE NEURAL-CONTROL;
D O I
10.1109/ACCESS.2019.2954135
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
This paper studies the adaptive asymptotic tracking problem for a class of unknown nonlinear systems in pure-feedback form. Different from the traditional literatures which only tackle the bounded tracking problem for pure-feedback systems, this paper investigates the asymptotic tracking problem by developing a novel controller design method. Moreover, the differentiable assumption on nonaffine functions is canceled, and only a mild semi-bounded assumption is required as the controllability condition. By utilizing Lyapunov theorem, it is proved that all the variables of the resulting closed-loop system are semi-globally uniformly ultimately bounded, and the output tracking error can converge to zero asymptotically by choosing design parameters appropriately. Finally, a simulation result is presented to verify the effectiveness of the proposed control scheme.
引用
收藏
页码:166721 / 166728
页数:8
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