Eur. Phys. J. Plus (2021) 136: 396
https://doi.org/10.1140/epjp/s13360-021-01382-3

Regular Article

Neuro-intelligent networks for Bouc–Wen hysteresis model for piezostage actuator

¹ Department of Electrical Engineering, Pakistan Institute of Engineering and Applied Sciences, 45650, Nilore, Islamabad, Pakistan
² Future Technology Research Center, National Yunlin University of Science and Technology, 123 University Road, Section 3, 64002, Douliou, Yunlin, Taiwan, ROC
³ School of Electrical and Electronic Engineering, The University of Adelaide, Adelaide, Australia
⁴ School of Electronics Engineering, Kyungpook National University, Daegu, South Korea
⁵ Department of Computer and Information Sciences, Pakistan Institute of Engineering and Applied Sciences, 45650, Nilore, Islamabad, Pakistan
⁶ Department of Mathematics, COMSATS University Islamabad, Attock Campus, 43600, Attock, Pakistan

^b rajamaz@yuntect.edu.tw

Received: 5 January 2021
Accepted: 30 March 2021
Published online: 13 April 2021

Abstract

Piezoelectric stage has become promising actuator for wide applications of micro-/nano-positioning systems represented mathematically with Bouc–Wen hysteresis model to examine the efficiency. In this investigation, the numerical study of piezostage actuator based on nonlinear Bouc–Wen hysteresis model is presented by neurocomputing intelligence via Levenberg–Marquardt backpropagated neural networks (LMB-NNs). Numerical computing strength of Adams method is implemented to generate a dataset of LMB-NNs for training, testing and validation process based on different scenarios of input voltage signals to piezostage actuator model. The performance of LMB-NNs of nano-positioning system model is validated through accuracy measures on means square error, histogram illustrations and regression analysis.