Neural Network Based Global Adaptive Dynamic Surface Tracking Control for Robot Manipulators

被引：0

作者：

Teng, Tao ^{[1
]}

Yang, Chenguang ^{[1
,2
]}

Xu, Bin ^{[3
]}

Li, Zhijun ^{[1
]}

机构：

[1] South China Univ Technol, Key Lab Autonomous Syst & Networked Control, Coll Automat Sci & Engn, Guangzhou 510640, Guangdong, Peoples R China

[2] Swansea Univ, Zienkiewicz Ctr Computat Engn, Swansea SA1 8EN, W Glam, Wales

[3] Northwestern Polytech Univ, Sch Automat, Xian, Peoples R China

来源：

IEEE ICARM 2016 - 2016 INTERNATIONAL CONFERENCE ON ADVANCED ROBOTICS AND MECHATRONICS (ICARM) | 2016年

关键词：

STRICT-FEEDBACK SYSTEMS; NONLINEAR-SYSTEMS; NN CONTROL;

D O I：

暂无

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

A neural network empowered dynamic surface control (DSC) technique is addressed for robot manipulators system with unknown dynamics. In comparison to the conventional adaptive neural control algorithms, which could guarantee semi-globally uniformly ultimate boundedness (SGUUB) only when neural approximation keeps effective, the scheme designed in this paper ensures globally uniformly ultimately bounded (GUUB) stability by integrating a switching mechanism which incorporates an additional robust controller to drag the transient state variables back when they go beyond the neural approximation region. Simulation studies on 2-joint robot manipulator have been carried out to validate the designed controller has excellent performance.

引用

页码：20 / 25

页数：6

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