Neural networks for nonlocal hysteresis function identification and compensation

We discuss the online identification of nonlocal static hysteresis functions, which are encountered in mechanical friction, magnetic materials, and piezoelectric actuators and causes problems by the design of controllers. We introduce a compensation method for friction in presliding regime based on the simplified Leuven friction model and on technology borrowed from neural networks. We present a solution how to identify the hysteresis caused by the friction and how to use this identified model for the compensation of the friction effects. Results from both simulations and experiments are shown.