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 offline 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.
%0 Journal Article
%1 Mbede2001
%A Mbede, Jean Bosco
%A Wei, Wu
%A Zhang, Qisen
%D 2001
%J Journal of Intelligent and Robotic Systems
%K Lyapunov avoidance, controller, dynamic fuzzy learning, manipulators networks, neural obstacle on-line recurrent robot stability,
%P 155-177
%T Fuzzy and Recurrent Network Motion Control among Dynamic Obstacles
for Robot Manipulators
%V 30
%X 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 offline 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.
@article{Mbede2001,
abstract = {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 offline 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.},
added-at = {2009-06-26T15:25:19.000+0200},
author = {Mbede, Jean Bosco and Wei, Wu and Zhang, Qisen},
biburl = {https://www.bibsonomy.org/bibtex/22b0af47b20e1e1922e39bb9cd13b2bd1/butz},
comment = {roboter lernt hindernissen auszuweichen},
description = {diverse cognitive systems bib},
interhash = {c028e061f3061775a16958288d51ac7f},
intrahash = {2b0af47b20e1e1922e39bb9cd13b2bd1},
journal = {Journal of Intelligent and Robotic Systems},
keywords = {Lyapunov avoidance, controller, dynamic fuzzy learning, manipulators networks, neural obstacle on-line recurrent robot stability,},
owner = {martin},
pages = {155-177},
timestamp = {2009-06-26T15:25:47.000+0200},
title = {Fuzzy and Recurrent Network Motion Control among Dynamic Obstacles
for Robot Manipulators},
volume = 30,
year = 2001
}