Research on Manipulator trajectory tracking with model approximation RBF neural network adaptive control

被引：6

作者：

Jiang, Jing ^{[1
]}

Pan, Linlin ^{[1
]}

Dai, Ying ^{[1
]}

Che, Long ^{[2
]}

机构：

[1] Shenyang Ligong Univ, Sch Automat & Elect Engn, Shenyang 110159, Liaoning, Peoples R China

[2] Nanjing Univ Sci & Technol, Sch Mech Engn, Nanjing 210094, Jiangsu, Peoples R China

来源：

2017 29TH CHINESE CONTROL AND DECISION CONFERENCE (CCDC) | 2017年

基金：

中国国家自然科学基金;

关键词：

Manipulator; Trajectory tracking; Neural network; Adaptive control; Model approximation;

D O I：

10.1109/CCDC.2017.7978199

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

According to the problem of manipulator trajectory tracking accuracy, model approximation RBF neural network adaptive control is applied in the manipulator trajectory tracking. In this paper, integral approximation method and model block approach method are used. The control law is designed. Finally, the computer simulation results show the different adaptive abilities and tracking performances. Mode block approximation method can get the short transiton time. The overshoot of integral approximation method is very small.

引用

页码：573 / 576

页数：4

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