Adaptive Neural Network Control for Constrained Robot Manipulators

被引：1
|
作者：
Wang, Gang [1 ]
Sun, Tairen [1 ]
Pan, Yongping [2 ]
Yu, Haoyong [2 ]
机构：
[1] Jiangsu Univ, Zhenjiang 212013, Peoples R China
[2] Natl Univ Singapore, Singapore 117583, Singapore
来源：
ADVANCES IN NEURAL NETWORKS, PT II | 2017年 / 10262卷
基金：
中国国家自然科学基金;
关键词：
Adaptive control; Backstepping; Neural network; System constraint; Barrier Lyapunov function; Robot manipulator; INCLUDING ACTUATOR DYNAMICS; SLIDING MODE-CONTROL; BACKSTEPPING CONTROL; TRACKING CONTROL; DESIGN;
D O I：
10.1007/978-3-319-59081-3_15
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
This paper presents an adaptive neural network (NN) control strategy for robot manipulators with uncertainties and constraints. Position, velocity and control input constraints are considered and tackled by introducing barrier Lyapunov functions in the backstepping procedure. The system uncertainties are estimated and compensated by a locally weighted online NN. The boundedness of the closed-loop control system and the feasibility of the proposed control law are demonstrated by theoretical analysis. The effectiveness of the proposed control strategy has been verified by simulation results on a robot manipulator.
引用
收藏
页码：118 / 127
页数：10
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