Adaptive neural network control with optimal number of hidden nodes for trajectory tracking of robot manipulators

被引：38
|
作者：
Liu, Chengxiang [1 ,2 ]
Zhao, Zhijia [1 ,2 ,3 ]
Wen, Guilin [1 ,2 ,3 ]
机构：
[1] Guangzhou Univ, Sch Mech & Elect Engn, Guangzhou 510006, Guangdong, Peoples R China
[2] Guangzhou Univ, Adv Technol Ctr Special Equipment, Guangzhou 510006, Guangdong, Peoples R China
[3] Guangzhou Univ, Ctr Intelligent Equipment & Network Connected Sys, Guangzhou 510006, Guangdong, Peoples R China
来源：
NEUROCOMPUTING | 2019年 / 350卷
基金：
中国国家自然科学基金;
关键词：
Optimal number of hidden neural nodes; Adaptive neural network control; Trajectory tracking of robot manipulators; Lyapunov method;
D O I：
10.1016/j.neucom.2019.03.043
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
In this paper, an adaptive neural network control with optimal number of hidden nodes and less computation is proposed for approximating the system uncertainty and tracking the trajectory of robot manipulators. Unlike the existing researches on adaptive neural network for robot manipulators, whose number of hidden nodes is fixed and determined through the trial and error, a new approach is proposed to obtain the optimal number of hidden nodes, in which the number of hidden nodes adapts to the trajectory variations and is capable of catching up with the optimal value and minimizing the tracking error. The proposed control scheme can avoid overfitting and underfitting problems and guarantee a better trajectory tracking. Mathematical proof for stability and convergence of the system is presented using Lyapunov method. In the end, simulations are performed to illustrate the effectiveness of the proposed approach. (C) 2019 Elsevier B.V. All rights reserved.
引用
收藏
页码：136 / 145
页数：10
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