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

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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.

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Abbreviations

b :

Network bias

c :

Damping coefficient

d :

Output displacement

e :

Error between actual and desired outputs

\(f\) :

Nonlinear function

h :

Hysteresis response

\(I\) :

Identity matrix

\(J\) :

Jacobian matrix

k :

Stiffness coefficient

LMB:

Levenberg–Marquardt backpropagation

m :

Mass of sliding object

MSE:

Mean square error

N :

Hidden layers neurons

NN:

Neural networks

\(p\) :

Downhill step

Q :

Step interval

R :

Regression value

T :

Time variable

W :

Weight matrix

X :

Input voltage

Z :

Number of sample data points

α, β, γ :

Hysteresis loop shape control parameters

Δ :

Piezoelectric coefficient

\(\lambda\) :

Nonnegative scalars of identity matrix

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Authors and Affiliations

  1. Department of Electrical Engineering, Pakistan Institute of Engineering and Applied Sciences, Nilore, Islamabad, 45650, Pakistan

    Sidra Naz

  2. Future Technology Research Center, National Yunlin University of Science and Technology, 123 University Road, Section 3, Douliou, Yunlin, 64002, Taiwan, ROC

    Muhammad Asif Zahoor Raja

  3. School of Electrical and Electronic Engineering, The University of Adelaide, Adelaide, Australia

    Ammara Mehmood

  4. School of Electronics Engineering, Kyungpook National University, Daegu, South Korea

    Ammara Mehmood

  5. Department of Computer and Information Sciences, Pakistan Institute of Engineering and Applied Sciences, Nilore, Islamabad, 45650, Pakistan

    Aneela Zameer

  6. Department of Mathematics, COMSATS University Islamabad, Attock Campus, Attock, 43600, Pakistan

    Muhammad Shoaib

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  1. Sidra Naz
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Correspondence to Muhammad Asif Zahoor Raja.

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Naz, S., Raja, M.A.Z., Mehmood, A. et al. Neuro-intelligent networks for Bouc–Wen hysteresis model for piezostage actuator. Eur. Phys. J. Plus 136, 396 (2021). https://doi.org/10.1140/epjp/s13360-021-01382-3

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