A Learning Approach for Feed-Forward Friction Compensation

Abstract

An experimental comparison of two feed-forward based friction compensation methods is presented. The first method is based on the LuGre friction model, using identified friction model parameters, and the second method is based on B-spline network, where the network weights are learned from experiments. The methods are evaluated and compared via experiments using a six axis industrial robot carrying out circular movements of different radii. The experiments show that the learning-based friction compensation gives an error reduction of the same magnitude as for the LuGre-based friction compensation.