Paper Info
Title
Fuzzy and Recurrent Neural Network Motion Control among Dynamic Obstacles for Robot Manipulators
Abstract
An integration of fuzzy controller and modified Elman neural networks (NN) approximation-based computed-torque controller is proposed for motion control of autonomous manipulators in dynamic and partially known environments containing moving obstacles. The fuzzy controller is based on artificial potential fields using analytic harmonic functions, a navigation technique common used in robot control. The NN controller can deal with unmodeled bounded disturbances and/or unstructured unmodeled dynamics of the robot arm. The NN weights are tuned on-line, with no off-line learning phase required. The stability of the closed-loop system is guaranteed by the Lyapunov theory. The purpose of the controller, which is designed as a neuro-fuzzy controller, is to generate the commands for the servo-systems of the robot so it may choose its way to its goal autonomously, while reacting in real-time to unexpected events. The proposed scheme has been successfully tested. The controller also demonstrates remarkable performance in adaptation to changes in manipulator dynamics. Sensor-based motion control is an essential feature for dealing with model uncertainties and unexpected obstacles in real-time world systems.
Year
DOI
Venue
2001
10.1023/A:1008194912825
Journal of Intelligent and Robotic Systems
Keywords
DocType
Volume
dynamic obstacle avoidance,fuzzy controller,Lyapunov stability,on-line learning,recurrent neural networks,robot manipulators
Journal
30
Issue
ISSN
Citations 
2
1573-0409
9
PageRank 
References 
Authors
0.93
12
3
Name
Order
Citations
PageRank
Jean Bosco Mbede1667.50
Wu Wei2242.33
Qisen Zhang3151.54