Adaptive neural network control of coordinated robotic manipulators with output constraint

被引：39
|
作者：
Zhang, Shuang [1 ]
Lei, Minjie [2 ]
Dong, Yiting [2 ]
He, Wei [3 ]
机构：
[1] Univ Elect Sci & Technol China, Sch Aeronaut & Astronaut, Chengdu 611731, Peoples R China
[2] Univ Elect Sci & Technol China, Ctr Robot & Sch Automat Engn, Chengdu 611731, Peoples R China
[3] Univ Sci & Technol Beijing, Sch Automat & Elect Engn, Beijing 100083, Peoples R China
来源：
IET CONTROL THEORY AND APPLICATIONS | 2016年 / 10卷 / 17期
基金：
中国国家自然科学基金;
关键词：
adaptive control; neurocontrollers; manipulators; uncertainty handling; stability; control system synthesis; radial basis function networks; function approximation; Lyapunov methods; closed loop systems; adaptive neural network control; coordinated robotic manipulators; output constraint; tracking control problem; instability handling; controller design; radial basis function neural network; bounded function approximation; continuous function approximation; barrier Lyapunov function; stability analysis; closed-loop system; CONTROL SCHEME; FUZZY CONTROL; SYSTEM; INPUT;
D O I：
10.1049/iet-cta.2016.0009
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
In this study, the authors aim to solve the tracking control problem of coordinated robotic manipulators. In order to handle with the uncertainties and instability of coordinated robotic manipulators and improve the performance of the system with output constraint, they design a controller by using radial basis function neural network which has the ability to approximate any bounded and continuous functions effectively. A barrier Lyapunov function is also introduced to prevent the violation of output constraint. The stability analysis of the closed-loop system is provided and the performance of the controller is verified through simulation.
引用
收藏
页码：2271 / 2278
页数：8
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