Adaptive neural dynamic surface control for a class of uncertain nonlinear systems with disturbances

被引：18
|
作者：
Cui, Yang [1 ]
Zhang, Huaguang [1 ]
Wang, Yingchun [1 ]
Zhang, Zhao [1 ]
机构：
[1] Northeastern Univ, Coll Informat Sci & Engn, Shenyang 110819, Peoples R China
来源：
NEUROCOMPUTING | 2015年 / 165卷
基金：
国家高技术研究发展计划(863计划); 中国国家自然科学基金;
关键词：
Dynamic surface control; RBF neural networks; Tracking control; Strict-feedback; Disturbances; DISCRETE-TIME; BACKSTEPPING CONTROL; STABILITY ANALYSIS; TRACKING CONTROL; FUZZY CONTROL; NN CONTROL; NETWORKS; ROBUST; DELAYS; DESIGN;
D O I：
10.1016/j.neucom.2015.03.004
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 paper, adaptive dynamic surface control is investigated for a class of uncertain nonlinear systems with unknown bounded disturbances in strict-feedback form. Dynamic surface control technique is connected with radial basis function neural networks (RBFNNs) based control framework to avoid the explosion problem of complexity. The composite laws are constructed by prediction error and compensated tracking error between system state and serial-parallel estimation model for NN weights updating. Using Lyapunov techniques, the uniformly ultimate boundedness stability of all the signals in the closed-loop systems is guaranteed. Simulation results illustrate the superiority of the proposed scheme and verify the theoretical analysis. (C) 2015 Elsevier B.V. All rights reserved.
引用
收藏
页码：152 / 158
页数：7
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