Sliding mode control based on neural network for giant magnetostrictive smart component

A new method for precise machining non-cylinder pin hole of piston by using giant magnetostrictive smart component is presented. To eliminate the impact of GMM smart component hysteresis and nonlinearity, a real-time hysteretic compensation control strategy combining a CMAC neural network feed forward controller and a sliding mode controller is proposed to implement the precision position tracking control of the smart component. The input data of CMAC neural network are the current smart component output and the output rate, the output of neural network is smart component input. The inverse hysteresis model of GMM smart component is achieved by CMAC network on-line learning. The model approximate error of CMAC neural network and the external disturbance is eliminated by using discrete sliding mode controller. 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.