Finite-Time Adaptive Control for a Class of Nonlinear Systems With Nonstrict Feedback Structure

被引：266
作者：
Sun, Yumei [1 ]
Chen, Bing [2 ]
Lin, Chong [1 ]
Wang, Honghong [1 ]
机构：
[1] Qingdao Univ, Qingdao 266071, Peoples R China
[2] Qingdao Univ, Inst Complex Sci, Qingdao 266071, Peoples R China
来源：
IEEE TRANSACTIONS ON CYBERNETICS | 2018年 / 48卷 / 10期
基金：
中国国家自然科学基金;
关键词：
Adaptive neural control; backstepping; finite time tracking; nonlinear systems; nonstrict feedback structure; TRACKING CONTROL; STOCHASTIC-SYSTEMS; DEAD ZONE; STABILIZATION; STABILITY; DESIGN;
D O I：
10.1109/TCYB.2017.2749511
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 focuses on finite-time adaptive neural tracking control for nonlinear systems in nonstrict feedback form. A semiglobal finite-time practical stability criterion is first proposed. Correspondingly, the finite-time adaptive neural control strategy is given by using this criterion. Unlike the existing results on adaptive neural/fuzzy control, the proposed adaptive neural controller guarantees that the tracking error converges to a sufficiently small domain around the origin in finite time, and other closed-loop signals are bounded. At last, two examples are used to test the validity of our results.
引用
收藏
页码：2774 / 2782
页数：9
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