Precision control of magnetostrictive actuator using dynamic recurrent neural network with hysteron

Abstract

A control strategy for precision position tracking of the magnetostrictive actuator (MA) with dominant hysteresis is proposed. In this strategy, a dynamic recurrent neural network with hysteron (DRNNH) is adopted as a feed-forward controller for on-line learning the inverse model of the MA to remove the effect of the hysteresis of the MA. A proportional-plus-derivative (PD) feedback controller is used to reduce the position tracking error. Simulation results validate the excellent performances of the control strategy. © Springer-Verlag Berlin Heidelberg 2005.