Realization of sorting & taxis of Preisach inverse hysteresis model using neural network

In order to compensate the hysteresis nonlinearity and to improve the precision of the nanometer positioning system with hysteresis in a piezo-ceramic actuator, this paper studies the inverse hysteresis modeling of the piezoceramic actuator. Taking both the wiping-out property and the modeling precision into consideration, a neural networks is proposed to realize the sorting & taxis model of hysteresis and to replace the reverse checking and interpolation method to reduce the error of the hysteresis modeling. A BP network with three layers is established, and the weight for every layer is obtained by training practical data. Based on the voltage and displacement extrema got from sorting and taxis, the input voltage of the piezoceramic actuator is obtained by using the neural network.Furthermore, several groups of experiment data are used to verify the accuracy of the proposed inverse model. Results indicate that this method using neural network reduces the average error of the input voltage to less than 1.5 V and the max error of the absolute value to less than 2.7 V. Compared with the reverse checking and interpolation method, this method effectively improves the precision of the Preisach inverse hysteresis model.