Robust Adaptive Control for Robotic Manipulators Based on RBFNN

被引：0
|
作者：
Xing, Bangsheng [1 ]
Zhang, Wenhui [1 ]
机构：
[1] Jiangsu Normal Univ, Sch Mech & Elect Engn, Xuzhou, Jiangsu, Peoples R China
来源：
ADVANCED DESIGN AND MANUFACTURING TECHNOLOGY III, PTS 1-4 | 2013年 / 397-400卷
关键词：
Neural network; Robotic manipulators; Robust control; Adaptive control; MODEL-FOLLOWING CONTROL; FEEDBACK-CONTROL;
D O I：
10.4028/www.scientific.net/AMM.397-400.1477
中图分类号：
TP39 [计算机的应用];
学科分类号：
081203 ; 0835 ;
摘要：
The rigid robotic manipulators is used in the mining industry more and more widely. An adaptive robust control algorithm of robotic manipulators based on radial basis function neural network(RBFNN) is proposed by the paper. Neural network controller is used to adaptive learn and compensate the unknown system, approach errors as disturbance are eliminated by robust controller. The weight adaptive laws on-line based on Lyapunov theory is designed. The robust controller was proposed based on H infinity theory. Above these assured the stability of the whole system, and L2 gain also was less than the index. This control scheme possesses great control accuracy and dynamic function. The simulation results show that the presented neural network control algorithm is effective.
引用
收藏
页码：1477 / 1481
页数：5
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