Fixed-time adaptive neural tracking control for a class of uncertain nonstrict nonlinear systems

被引：158
|
作者：
Ba, Desheng [1 ]
Li, Yuan-Xin [1 ,2 ]
Tong, Shaocheng [1 ]
机构：
[1] Liaoning Univ Technol, Coll Sci, Jinzhou 121001, Liaoning, Peoples R China
[2] Qingdao Univ, Inst Complex Sci, Qingdao 266071, Shandong, Peoples R China
来源：
NEUROCOMPUTING | 2019年 / 363卷
关键词：
Adaptive control; Nonstrict feedback nonlinear systems; Backstepping; Fixed-time control; Neural networks; OUTPUT-FEEDBACK CONTROL; NETWORK CONTROL; FUZZY CONTROL; STABILIZATION;
D O I：
10.1016/j.neucom.2019.06.063
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
This paper addresses the fixed-time adaptive neural control of nonstrict feedback nonlinear system. With the help of neural networks and the backstepping technical, a fixed-time adaptive neural control scheme is presented. To guarantee closed-loop stability, a new semiglobal practical fixed-time stability (SPFTS) criterion is set up. Based on the established SPFTS criterion, we can show that both the tracking performance and the closed-loop stability can be preserved in a fixed time via the presented approach. Compared with the existing finite-time control, the convergence time of the propose fixed-time control scheme does not rely on the initial states. Finally, the proposed technique is demonstrated with simulation results. (C) 2019 Elsevier B.V. All rights reserved.
引用
收藏
页码：273 / 280
页数：8
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