Nonlocal hysteresis function identification and compensation with neural networks

This paper discusses the on-line identification of nonlocal static hysteresis functions, which are encountered in mechanical friction, magnetic materials, and piezoelectric actuators and cause problems in the design of controllers. In this article a new compensation method for friction in presliding regime is introduced that is 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. The solution can be used for on-line identification and compensation. Results from both simulations and experiments will be shown.