A New Robotic Drive Joint Friction Compensation Mechanism Using Neural Networks

Abstract

The knowledge of realistic dynamic models to robotic actuators would be of great aid in the synthesis of control laws to robot manipulators, mainly in cases of great precision robotic or even for manipulators with flexible links. In this paper we present a training scheme and propose a structure of neural network (NN) to learn the friction torque of a geared motor drive joint robotic actuator. To train the NN was used experimental data obtained by an harmonic-drive actuator, equipped with an encoder to measure the rotor angular position. It was considered the motor torque and the rotor angular velocity as NN input, while the friction torque was the only output, which was used in the proposition of a non-linear friction compensation mechanism. The experimental results attested the efficiency of the NN friction estimation and compensation with the proposed mechanism.