A two-layer recurrent neural network for real-time control of redundant manipulators with torque minimization

被引：0

作者：

Tang, WS ^{[1
]}

Wang, J ^{[1
]}

机构：

[1] Chinese Univ Hong Kong, Dept Mech & Automat Engn, Shatin, New Territories, Hong Kong

来源：

1998 IEEE INTERNATIONAL CONFERENCE ON SYSTEMS, MAN, AND CYBERNETICS, VOLS 1-5 | 1998年

关键词：

D O I：

暂无

中图分类号：

TP3 [计算技术、计算机技术];

学科分类号：

0812 ;

摘要：

A recurrent neural network for kinematic control of redundant robot manipulators with torque minimization is presented. The proposed recurrent neural network is composed of two bidirectionally connected layers of neuron arrays. While the command signals of desired acceleration of the end-effector are fed into the input layer, the output layer generates the joint acceleration vector of the manipulator with joint torques being minimized. The proposed recurrent neural network is shown to be capable of asymptotic tracking of trajectory for the redundant manipulators with minimized joint torques.

引用

页码：1720 / 1725

页数：6