Open AccessA BP Neural Network Modeling Method Based on Global Error for the Hysteresis of Piezoelectric Actuator
Author(s) -
Yanyan Wang,
Hai Guo
Publication year - 2019
Publication title -
iop conference series. materials science and engineering
Language(s) - English
Resource type - Journals
eISSN - 1757-899X
pISSN - 1757-8981
DOI - 10.1088/1757-899x/585/1/012070
Subject(s) - hysteresis , artificial neural network , nonlinear system , control theory (sociology) , actuator , piezoelectricity , computer science , creep , topology (electrical circuits) , materials science , engineering , control (management) , artificial intelligence , physics , electrical engineering , condensed matter physics , quantum mechanics , composite material
Piezoelectric actuator (PZT) is used widely in nano positioning, nano measurement and nano mechanics. However, its hysteresis, creep and nonlinearity affect the positioning accuracy seriously, especially the hysteresis. The paper proposes a BP neural network modeling method based on global error to model the hysteresis of the PZT. The network contains input, hidden and output layers. Its training goal is based on global errors. And the network could adjust the connection weight of the network dynamically according to different inputs till the global errors reduce to the threshold. Experiments prove that the method could fit the hysteresis curves of the PZT well. And the training errors could be controlled under 0.05.