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
收藏
页码：20 / 25
页数：6
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