ROBUST NEURAL NETWORK CONTROL OF ROBOTIC MANIPULATORS VIA SWITCHING STRATEGY

被引：0

作者：

Yu, Lei ^{[1
,2
]}

Fei, Shumin ^{[3
]}

Huang, Jun ^{[1
]}

Li, Yongmin ^{[4
]}

Yang, Gang ^{[5
]}

Sun, Lining ^{[1
]}

机构：

[1] Soochow Univ, Sch Mech & Elect Engn, Suzhou 215021, Peoples R China

[2] Woosong Univ, Sch Railrd & Transportat, Dept Railrd Elect Syst Engn, Taejon, South Korea

[3] Southeast Univ, Minist Educ, Key Lab Measurement & Control Complex Syst Engn, Nanjing, Jiangsu, Peoples R China

[4] Huzhou Teachers Coll, Sch Sci, Huzhou, Peoples R China

[5] Huaiyin Inst Techlon, Digital Manufacture Technol Key Lab JiangSu Prov, Huaiyin, Peoples R China

来源：

KYBERNETIKA | 2015年 / 51卷 / 02期

基金：

中国博士后科学基金; 中国国家自然科学基金;

关键词：

robotic manipulators; switching control strategy; RBF neural networks; multiple Lyapunov function; TRACKING CONTROL; STABILIZATION; SYSTEMS;

D O I：

暂无

中图分类号：

TP3 [计算技术、计算机技术];

学科分类号：

0812 ;

摘要：

In this paper, a robust neural network control scheme for the switching dynamical model of the robotic manipulators has been addressed. Radial basis function (RBF) neural networks are employed to approximate unknown functions of robotic manipulators and a compensation controller is designed to enhance system robustness. The weight update law of the robotic manipulator is based on switched multiple Lyapunov function method and the periodically switching law which is suitable for practical implementation is constructed. The proposed control scheme can guarantee that the resulting closed-loop switched system is asymptotically Lyapunov stable and the tracking error performance of the control system is well reached. Finally, a simulation example of two-link robotic manipulators is shown to illustrate the effectiveness of the proposed control method.

引用

页码：309 / 320

页数：12

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