Modeling of Ultra-Precision Piezodriven Positioning System

Abstract

A neural network based approach for the identification of the rate-dependent hysteresis in the piezoelectric actuators is proposed in this article. In this method, a dynamic hysteresis operator for expanded input space is proposed to extract the change-tendency and rate-dependency of the dynamic hysteresis, the parameters of the hysteretic operator is identified using genetic algorithm. An expanded input space involving the original input variable and the new operator is constructed. Thus, based on the expanded input space, the neural networks can be utilized to approximate the behavior of the rate-dependent hysteresis. Furthermore, the dynamic performance of the model is improved because of the existence of dynamic operator. Finally, the method is used to the modeling of hysteresis in a piezoelectric actuator. The experimental results are presented to verify the effectiveness of the proposed approach.