Direct adaptive NN control for a class of discrete-time nonlinear strict-feedback systems

被引：43
|
作者：
Liu, Yan-Jun [1 ]
Wen, Guo-Xing [1 ]
Tong, Shao-Cheng [1 ]
机构：
[1] Liaoning Univ Technol, Sch Sci, Jinzhou 121001, Liaoning, Peoples R China
来源：
NEUROCOMPUTING | 2010年 / 73卷 / 13-15期
关键词：
Neural networks; Adaptive control; Nonlinear systems; GROSSBERG NEURAL-NETWORKS; ROBUST STABILITY; TRACKING CONTROL;
D O I：
10.1016/j.neucom.2010.06.001
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
Based on the backstepping technique, a direct adaptive neural network control algorithm is proposed for a class of uncertain nonlinear discrete-time systems in the strict-feedback form. Neural networks are utilized to approximate unknown functions, and a stable adaptive neural backstepping controller is synthesized. It is proven that all the signals in the closed-loop system are semi-globally uniformly ultimately bounded (SGUUB) and the tracking error converges to a small neighborhood of zero by choosing the design parameters appropriately. Compared with the existing results for discrete-time systems, the proposed algorithm needs only less parameters to be adjusted online, therefore, it can reduce online computation burden. A simulation example is employed to illustrate the effectiveness of the proposed algorithm. Crown Copyright (C) 2010 Published by Elsevier B.V. All rights reserved.
引用
收藏
页码：2498 / 2505
页数：8
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