DocumentCode :
706802
Title :
A force control approach of robotic manipulators in non-rigid environments
Author :
Baptista, Luis Filipe ; Sa da Costa, Jose M. G.
Author_Institution :
Dept. of Mech. Eng., Tech. Univ. of Lisbon, Lisbon, Portugal
fYear :
1999
fDate :
Aug. 31 1999-Sept. 3 1999
Firstpage :
2777
Lastpage :
2782
Abstract :
In this paper, the application of an impedance control scheme to drive a robot with a desired force profile in the presence of non-rigid environments is considered. Fine force control obtained with the usual impedance based control schemes reveal some difficulties especially for non-rigid environments. Moreover, robot dynamic model and environmental uncertainties, can seriously affect the force tracking performance. In this paper, alternative control methodologies like predictive control and neural network compensation are used to design an impedance force control with an accurate force tracking performance. Thus, a predictive algorithm is designed to compute the virtual trajectory for the impedance controller. Further, the constrained acceleration produced by the impedance controller is compensated by an on-line neural network scheme in order to reduce the force errors. The performance of the proposed force controller is illustrated by simulation for a two degree-of-freedom PUMA 560 robot, which end-effector is forced to move along a surface on the vertical plane. Simulation results reveal an accurate force tracking performance for the proposed control scheme in the presence of non-rigid environments.
Keywords :
compensation; force control; manipulators; neurocontrollers; predictive control; trajectory control; force control approach; force profile; force tracking performance; impedance based control scheme; impedance control scheme; impedance force control design; neural network compensation; nonrigid environment; predictive control; robotic manipulator; two degree-of-freedom PUMA 560 robot; virtual trajectory; Acceleration; Artificial neural networks; Force; Force control; Impedance; Robots; Force control; artificial neural networks; impedance control; on-line learning; predictive control;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Control Conference (ECC), 1999 European
Conference_Location :
Karlsruhe
Print_ISBN :
978-3-9524173-5-5
Type :
conf
Filename :
7099747
Link To Document :
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