Precision position tracking control for giant magnetostrictive smart component based on CMAC self-learning

A new method for precise machining non-cylinder pin hole of piston by using embedded giant magnetostrictive smart component is presented. The intrinsic hysteresis observed in giant magnetostrictive material (GMM) has impaired the motion accuracy. A new kind of architecture of neural network is proposed to approximate the smart components hysteresis. The inverse hysteresis model of GMM smart component is achieved by CMAC network on-line learning. A real-time hysteresis compensation control strategy combining a CMAC neural network feed forward controller and a proportional derivative (PD) feedback controller is proposed to implement the precision position tracking control of the smart component. Simulation results show that this control strategy can on-line obtain inverse hysteresis model of the smart component, eliminate the hysteretic nonlinear impact and achieve the precision control of the smart component.