Development of a novel dynamic friction model and precise tracking control using adaptive back-stepping sliding mode controller

The LuGre model which has been widely used to describe the friction phenomenon for mechanical systems consists of stiffness and viscous terms. The stiffness term of the LuGre model shows the friction torque to act linearly for the internal state of friction dynamics. Thus it cannot represent the hysteresis phenomenon of friction in the pre-sliding phase. Especially the hysteresis has the non-local memory characteristics. In this paper, the non-local memory hysteresis phenomenon is analyzed through experiments and the improved friction model using the Preisach model is proposed. In order to implement the Preisach model, the neural network algorithm is used to increase the efficiency of the Preisach algorithm. Based on the improved friction model, the adaptive back-stepping sliding mode controller (SMC) is designed to improve tracking performance in the sliding and pre-sliding phases. To evaluate the performance of the proposed friction control system, experiments are executed for a ball-screw servo system and the satisfactory results are shown.