The piezoelectric ceramics have the property of hysteresis and nonlinearity

and the change rule is unknown and uncertain. It is difficult to build a high accuracy mathematical model simply using the traditional method. In order to improve the model accuracy of micro-displacement stage driven by piezoelectric ceramics

a new modeling method based on neural network is proposed in this paper. The structure and the modeling method of the stage are analyzed. Because of the advantages of the neural network in self-learn and self-adapting

the structure and the parameters can be adjusted on line to reduce the error of the stage model

and more exact information can be provided for the control system. Through training the net model by the stage displacement data

experiment results show that the average error and the maximum error are reduced to 80nm and 100 nm within 80 μm journey respectively

which satisfies the precision requirement of nanometer positioning.