%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 }