Paper Info
Title
Robust Fuzzy and Recurrent Neural Network Motion Control among Dynamic Obstacles for Robot Manipulators
Abstract
An integration of a 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 navigation technique of robot control using artificial potential fields is based on the fuzzy controller. The NN controller can deal with unmodeled bounded disturbances and or unstructured unmodeled dynamics of the robot arm. The NN weights are tuned online, 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
2000
10.1109/ROBOT.2000.846345
ICRA
Keywords
Field
DocType
real time,path planning,neural networks,artificial neural networks,robot arm,motion control,recurrent neural network,servo systems,fuzzy control,servo system,stability,recurrent neural networks,computer networks,neuro fuzzy,navigation,robot control,robust control
Robot control,Motion control,Robotic arm,Control theory,Control theory,Fuzzy logic,Recurrent neural network,Control engineering,Engineering,Fuzzy control system,Robust control
Conference
Volume
Issue
ISSN
3
1
1050-4729
Citations 
PageRank 
References 
1
0.45
3
Authors
3
Name
Order
Citations
PageRank
Jean Bosco Mbede1667.50
Xinhan Huang211419.04
Min Wang3377.90