Title :
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
fDate :
27 Jun-2 Jul 1994
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;
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
DOI :
10.1109/ICNN.1994.374671
