An extended LuGre model for estimating nonlinear frictions in feed drive systems of machine tools
Tiandong Xi, Tomoya Fujita, Sebastian Kehne, Ryosuke Ikeda, Marcel Fey, Christian Brecher
Abstract
Nonlinear frictions in feed drive axes affect the control accuracy and dynamic of the machine tools. Conventional dynamic friction models neglect the position-dependent friction, caused by manufacturing errors and misalignment as well as the environment. This paper proposes an extended LuGre friction model for estimating the dynamic and position-dependent friction effects. The conventional LuGre model is modified by a position-dependent term, which describes the nonlinear friction behavior of machine axes with long travel distance. An online data-driven identification approach is developed to fast parametrize and update the model. The suggested method is validated on a real machine tool.