Abstract
Hysteresis and nonlinearity of piezo actuator are the major factors affecting the motion accuracy in controlling a micro system. The prediction accuracy of classical Preisach model could be improved only by mass experiments for measuring hysteresis. A model based on BP neural networks was proposed to improve the prediction accuracy. Because the stoke of piezo actuator have relation to historical extrema inputs, the input of the model are current exciting voltage, historical voltage at nearest turning point and its corresponding stroke and the output is piezo actuator’s stroke. Results of simulation and experiments show that the proposed hysteresis model can exactly describe and predict the hysteresis of piezo actuator compare with traditional bilinear interpolation and has the better generalization ability.
This work is partially supported by Graduate Education Innovation Project, Jiangsu province Corresponding author. Tel: +86-13395226900; E-mail address: hopeasy@163.com
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Yang, X., Li, W., Wang, Y., Ye, G. (2008). Modeling Hysteresis in Piezo Actuator Based on Neural Networks. In: Kang, L., Cai, Z., Yan, X., Liu, Y. (eds) Advances in Computation and Intelligence. ISICA 2008. Lecture Notes in Computer Science, vol 5370. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-92137-0_32
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DOI: https://doi.org/10.1007/978-3-540-92137-0_32
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