A two-layer recurrent neural network for kinematic control of redundant manipulators

Author

Wang, Jun ; Hu, Qingni ; Jiang, Dan-chi

Author_Institution

Dept. of Mech. & Autom. Eng., Chinese Univ. of Hong Kong, Shatin, Hong Kong

Volume

3

fYear

1997

fDate

10-12 Dec 1997

Firstpage

2507

Abstract

A recurrent neural network is presented for the kinematic control of kinematically redundant robot manipulators. The proposed recurrent neural network is composed of two bidirectionally connected layers of neuron arrays. While the signals of desired velocity of the end-effector are fed into the input layer, the output layer generates the joint velocity vector of the manipulator. The proposed recurrent neural network is shown to be capable of asymptotic tracking for the motion control of kinematically redundant manipulators

Keywords

manipulator kinematics; motion control; neurocontrollers; recurrent neural nets; stability; tracking; velocity control; asymptotic tracking; joint velocity vector; kinematic control; motion control; neuron arrays; recurrent neural network; redundant manipulators; stability; Automatic control; Closed-form solution; Jacobian matrices; Kinematics; Manipulators; Nonlinear equations; Orbital robotics; Recurrent neural networks; Robot sensing systems; Robotics and automation;

fLanguage

English

Publisher

ieee

Conference_Titel

Decision and Control, 1997., Proceedings of the 36th IEEE Conference on

Conference_Location

San Diego, CA

ISSN

0191-2216

Print_ISBN

0-7803-4187-2

Type

conf

DOI

10.1109/CDC.1997.657673

Filename

657673