Dynamic external force feedback loop control of a robot manipulator using a neural compensator - Application to the trajectory following in an unknown environment

Farid Ferguene; Redouane Toumi

International Journal of Applied Mathematics and Computer Science (2009)

  • Volume: 19, Issue: 1, page 113-126
  • ISSN: 1641-876X

Abstract

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Force/position control strategies provide an effective framework to deal with tasks involving interaction with the environment. One of these strategies proposed in the literature is external force feedback loop control. It fully employs the available sensor measurements by operating the control action in a full dimensional space without using selection matrices. The performance of this control strategy is affected by uncertainties in both the robot dynamic model and environment stiffness. The purpose of this paper is to improve controller robustness by applying a neural network technique in order to compensate the effect of uncertainties in the robot model. We show that this control strategy is robust with respect to payload uncertainties, position and environment stiffness, and dry and viscous friction. Simulation results for a three degrees-of-freedom manipulator and various types of environments and trajectories show the effectiveness of the suggested approach compared with classical external force feedback loop structures.

How to cite

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Farid Ferguene, and Redouane Toumi. "Dynamic external force feedback loop control of a robot manipulator using a neural compensator - Application to the trajectory following in an unknown environment." International Journal of Applied Mathematics and Computer Science 19.1 (2009): 113-126. <http://eudml.org/doc/207913>.

@article{FaridFerguene2009,
abstract = {Force/position control strategies provide an effective framework to deal with tasks involving interaction with the environment. One of these strategies proposed in the literature is external force feedback loop control. It fully employs the available sensor measurements by operating the control action in a full dimensional space without using selection matrices. The performance of this control strategy is affected by uncertainties in both the robot dynamic model and environment stiffness. The purpose of this paper is to improve controller robustness by applying a neural network technique in order to compensate the effect of uncertainties in the robot model. We show that this control strategy is robust with respect to payload uncertainties, position and environment stiffness, and dry and viscous friction. Simulation results for a three degrees-of-freedom manipulator and various types of environments and trajectories show the effectiveness of the suggested approach compared with classical external force feedback loop structures.},
author = {Farid Ferguene, Redouane Toumi},
journal = {International Journal of Applied Mathematics and Computer Science},
keywords = {force/position control; external structure; neural control; robot manipulator},
language = {eng},
number = {1},
pages = {113-126},
title = {Dynamic external force feedback loop control of a robot manipulator using a neural compensator - Application to the trajectory following in an unknown environment},
url = {http://eudml.org/doc/207913},
volume = {19},
year = {2009},
}

TY - JOUR
AU - Farid Ferguene
AU - Redouane Toumi
TI - Dynamic external force feedback loop control of a robot manipulator using a neural compensator - Application to the trajectory following in an unknown environment
JO - International Journal of Applied Mathematics and Computer Science
PY - 2009
VL - 19
IS - 1
SP - 113
EP - 126
AB - Force/position control strategies provide an effective framework to deal with tasks involving interaction with the environment. One of these strategies proposed in the literature is external force feedback loop control. It fully employs the available sensor measurements by operating the control action in a full dimensional space without using selection matrices. The performance of this control strategy is affected by uncertainties in both the robot dynamic model and environment stiffness. The purpose of this paper is to improve controller robustness by applying a neural network technique in order to compensate the effect of uncertainties in the robot model. We show that this control strategy is robust with respect to payload uncertainties, position and environment stiffness, and dry and viscous friction. Simulation results for a three degrees-of-freedom manipulator and various types of environments and trajectories show the effectiveness of the suggested approach compared with classical external force feedback loop structures.
LA - eng
KW - force/position control; external structure; neural control; robot manipulator
UR - http://eudml.org/doc/207913
ER -

References

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