Friction modelling and compensation for motion control using hybrid neural network models

This paper investigates artificial neural network (ANN) based modelling and compensation of nonlinear friction which is a major cause of performance degradation in servo mechanisms. Different friction modelling and compensation schemes have been reviewed and neural network based hybrid compensation methods are proposed and experimentally tested on a direct drive servo mechanism. Inertial dynamics is assumed to be constant and a PD type control is deployed for the servo feedback without the motor's electrical dynamics. ANN based techniques resulted in good performance when compared with the experimentally obtained friction model and parametric adaptive models. Advantages and the implementation aspects of the proposed methods are also discussed.