Support vector machine networks for friction modeling

This paper presents a novel model-free parameterization approach of friction modeling for servo-motion systems, where support vector machine networks parameterize the static friction behavior. In training such network via SVM regression, the effort of accounting for the complexity variation of the static friction mapping is made in terms of varying smoothness and error-tolerance constraints. It is experimentally demonstrated that the proposed SVM networks can achieve satisfactory friction predictions.