Hysteresis modeling with deep learning network based on Preisach model

Aiming at the weak generalization ability of traditional piezoelectric scanner hysteresis models, a deep learning network based on Preisach model is proposed to establish the hysteresis model for piezoelectric scanners, which improves the learning and generalization ability of the model. Specifically, first, considering the advantage of deep learning network in feature extraction, a deep learning layer comprising two convolution layers, a pool layer, an expansion layer, and a deep feature layer is established to extract the characteristic information of the input voltage signal. Afterwards, a Fourier transform layer is used to calculate the frequency of input signal, which is then input to the nonlinear layer to output a frequency-dependent nonlinear term, subsequently, the nonlinear term is multiplied by the hysteresis unit of the Preisach model to obtain the frequency-dependent model output vector. Finally, the output displacement of the whole depth learning network is obtained by multiplying the feature vector of the depth learning layer with the output vector of the Preisach model. In the section of network training and testing, 16 groups of input and output signals collected by the capacitance displacement sensor are used to train the deep learning network to get the weight parameters, and the other 8 groups of input and output data are tested on the deep network. The results show that the proposed deep learning network improves the generalization ability of the model while obtaining the high precision hysteresis model. © 2018, Editorial Department of Control Theory & Applications South China University of Technology. All right reserved.