The paper deals with a neural network control for the gravity compensation of a parallel kinematics robot. The network has been designed in a simulation environment then it has been implemented in robot’s controller by using an FPGA device that is part of an embedded system. After the training phase, several experiments have been performed on the prototype manipulator and the related datasets have been collected and elaborated. In the end, a comparative analysis has shown the improved performance of the neural network controller with respect to the inverse dynamics one, mainly due to the well-known difficulties in deriving explicit models of friction and play in the join

Design and Experimentation of a Neural Network Controller for a Spherical Parallel Robot

CARBONARI, LUCA;CALLEGARI, Massimo
2012

Abstract

The paper deals with a neural network control for the gravity compensation of a parallel kinematics robot. The network has been designed in a simulation environment then it has been implemented in robot’s controller by using an FPGA device that is part of an embedded system. After the training phase, several experiments have been performed on the prototype manipulator and the related datasets have been collected and elaborated. In the end, a comparative analysis has shown the improved performance of the neural network controller with respect to the inverse dynamics one, mainly due to the well-known difficulties in deriving explicit models of friction and play in the join
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11566/79392
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