A fuzzy-neural-network-based visual feedback learning control for robot manipulators

Author

Suh, Il Hong ; Kim, Tae Won

Author_Institution

Dept. of Electron. Eng., Hanyang Univ., Kyeongki, South Korea

Volume

5

fYear

1994

fDate

27 Jun-2 Jul 1994

Firstpage

2781

Abstract

A visual feedback learning control algorithm is proposed for a robot manipulator equipped with joint position servos employing fuzzy-membership-function-based neural networks (FMFNN), where weightings of FMFNN´s are adjusted in such a way that the robot manipulator with an eye in hand is capable of not only tracking a moving object along the line of sight but also stopping in front of a static object, wherever it is. The training mechanisms of FMFNN are extended to be applied to the control of dynamic systems. To show the validity of the proposed algorithm, several numerical examples are illustrated for a robot manipulator equipped with position servos

Keywords

feedback; fuzzy control; fuzzy neural nets; intelligent control; learning (artificial intelligence); learning systems; manipulators; neurocontrollers; position control; robot vision; dynamic systems; eye in hand; fuzzy-neural-network-based visual feedback learning control; joint position servos; moving object tracking; robot manipulators; training mechanisms; Computer vision; Control systems; Feedback; Intelligent robots; Jacobian matrices; Manipulator dynamics; Neural networks; Robot control; Robot motion; Servomechanisms;

fLanguage

English

Publisher

ieee

Conference_Titel

Neural Networks, 1994. IEEE World Congress on Computational Intelligence., 1994 IEEE International Conference on

Conference_Location

Orlando, FL

Print_ISBN

0-7803-1901-X

Type

conf

DOI

10.1109/ICNN.1994.374671

Filename

374671