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Arabian Journal for Science and Engineering

, Volume 44, Issue 8, pp 6941–6949 | Cite as

Implementation and Identification of Preisach Parameters: Comparison Between Genetic Algorithm, Particle Swarm Optimization, and Levenberg–Marquardt Algorithm

  • H. MarouaniEmail author
  • K. Hergli
  • H. Dhahri
  • Y. Fouad
Research Article - Electrical Engineering
First Online:
  • 78 Downloads

Abstract

Electromagnetic simulations of devices with soft materials and the study of the influence of cutting edge deformations/stresses on the degradation of magnetic performances require the identification of hysteresis parameters. Hence, computationally efficient implementation and identification of the hysteresis model is needed to obtain a solution with high accuracy within a reasonable time. Many studies on hysteresis identification have been reported, but few compare different methods to identify the optimal one. In the present study, we focus on the Preisach hysteresis model combined with the Lorentz modified distribution function to describe the magnetic behavior of a fully processed nonoriented Fe–3wt% Si steel sheet under static excitation. Three different identification techniques are implemented and evaluated: particle swarm optimization (PSO), genetic algorithms (GAs), and nonlinear least-squares approximation based on the Levenberg–Marquardt (LM) method. We evaluate each approach with regard to the accuracy, central processing unit computation time, and repeatability of the results. All the techniques perform well in this high-nonlinearity problem. The root-mean-square error is < 3%. However, the implementation of the GA is more complex than the PSO and LM methods. The optimized parameters are obtained in a few minutes in the case of the LM method, but a few hours are required for both the other techniques. Therefore, the LM method is the most suitable technique for the identification of Preisach hysteresis.

Keywords

Magnetic hysteresis Preisach Particle swarm optimization Genetic algorithms Nonlinear least-squares approximation 
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Notes

Acknowledgements

The authors thank the RSSU at King Saud University (Riyadh) for the technical support. The authors extend their sincere appreciation to the Deanship of Scientific Research at King Saud University for its funding of this research through Research Group No. RG-1439/007.

References

  1. 1.
    Preisach, F.: Über die magnetische Nachwirkung. Z. für Phys. 94, 277–302 (1935)CrossRefGoogle Scholar
  2. 2.
    Zhou, M.; Wang, S.; Gao, W.: Hysteresis modeling of magnetic shape memory alloy actuator based on Krasnosel’skii-Pokrovskii model. Sci. World J. 2013, 1–7 (2013)Google Scholar
  3. 3.
    Al Janaideh, M.; Aljanaideh, O.: Further results on open-loop compensation of rate-dependent hysteresis in a magnetostrictive actuator with the Prandtl–Ishlinskii model. Mech. Syst. Signal Proc. 104, 835–850 (2018)CrossRefGoogle Scholar
  4. 4.
    Ismail, M.; Ikhouane, F.; Rodellar, J.: The hysteresis Bouc–Wen model, a survey. Arch. Comput. Methods Eng. 16, 161–188 (2009)CrossRefzbMATHGoogle Scholar
  5. 5.
    Padthe, A.K.; Drincic, B.; Oh, J.; Fassois, S.D.; Bernstein, D.S.: Duhem modeling of friction-induced hysteresis. IEEE Control Syst. 28, 90–107 (2008)zbMATHGoogle Scholar
  6. 6.
    Jiles, D.C.; Atherton, D.L.: Theory of ferromagnetic hysteresis (invited). J. Appl. Phys. 55, 2115–2120 (1984)CrossRefGoogle Scholar
  7. 7.
    Hassani, V.; Tjahjowidodo, T.; Do, T.N.: A survey on hysteresis modeling, identification and control. Mech. Syst. Signal Proc. 49, 209–233 (2014)CrossRefGoogle Scholar
  8. 8.
    Worden, K.; Manson, G.: On the identification of hysteretic systems. Part I: fitness landscapes and evolutionary identification. Mech. Syst. Signal Proc. 29, 201–212 (2012)CrossRefGoogle Scholar
  9. 9.
    Kwok, N.M.; et al.: A novel hysteretic model for magnetorheological fluid dampers and parameter identification using particle swarm optimization. Sens. Actuators A 132, 441–451 (2006)CrossRefGoogle Scholar
  10. 10.
    Wang, G.; Chen, G.: Identification of piezoelectric hysteresis by a novel Duhem model based neural network. Sens. Actuators A 264, 282–288 (2007)CrossRefGoogle Scholar
  11. 11.
    Hergli, K.; Marouani, H.; Zidi, M.: Numerical determination of Jiles–Atherton hysteresis parameters: magnetic behavior under mechanical deformation. Phys. B Condens. Matter. 549, 74–81 (2018)CrossRefGoogle Scholar
  12. 12.
    Rémond, R.; Almeida, G.; Perré, P.: The gripped-box model: a simple and robust formulation of sorption hysteresis for lignocellulosic materials. Constr. Build. Mater. 170, 716–724 (2018)CrossRefGoogle Scholar
  13. 13.
    Ko, Y.-R.; et al.: Direct identification of generalized Prandtl–Ishlinskii model inversion for asymmetric hysteresis compensation. ISA Trans. 70, 209–218 (2017)CrossRefGoogle Scholar
  14. 14.
    Zheng, J.; et al.: Hybrid genetic algorithms for parameter identification of a hysteresis model of magnetostrictive actuators. Neurocomputing 70, 749–761 (2007)CrossRefGoogle Scholar
  15. 15.
    Ortiz, G.A.; Alvarez, D.A.; Bedoya-Ruíz, D.: Identification of Bouc–Wen type models using multi-objective optimization algorithms. Comput. Struct. 114–115, 121–132 (2013)CrossRefGoogle Scholar
  16. 16.
    Azzerboni, B.; et al.: A fuzzy model of scalar hysteresis on soft magnetic materials. Phys. B Condens. Matter. 343, 132–136 (2004)CrossRefGoogle Scholar
  17. 17.
    Hubert, O.; Hirsinger, L.; Hug, E.: Experimental influence of internal stresses on the anhysteretic curve of 3% SiFe nonoriented alloys. J. Magn. Magn. Mater. 196–197, 322–324 (1999)CrossRefGoogle Scholar
  18. 18.
    Iordache, V.E.; Hug, E.; Buiron, N.: Magnetic behaviour versus tensile deformation mechanisms in a non-oriented Fe–(3 wt.%)Si steel. Mater. Sci. Eng. A 359, 62–74 (2003)CrossRefGoogle Scholar
  19. 19.
    Hergli, K.; Marouani, H.; Zidi, M.: Identification of Preisach hysteresis model parameters using genetic algorithms. J. King Saud Univ.-Sci. (in press) (2018)Google Scholar
  20. 20.
    Li, J.; et al.: Parameter estimation algorithms for Hammerstein output error systems using Levenberg–Marquardt optimization method with varying interval measurements. J. Frankl. Inst. 354, 316–331 (2017)MathSciNetCrossRefzbMATHGoogle Scholar
  21. 21.
    Gavin, H.P.: The Levenberg–Marquardt method for nonlinear least squares curve-fitting problems. Dep. Civ. Environ. Eng. Duke Univ. (2016)Google Scholar
  22. 22.
    Bledsoe, K.C.; Favorite, J.A.; Aldemir, T.: A comparison of the covariance matrix adaptation evolution strategy and the Levenberg–Marquardt method for solving multidimensional inverse transport problems. Ann. Nucl. Energy 38, 897–904 (2011)CrossRefGoogle Scholar
  23. 23.
    Bhoskar, T.; et al.: Genetic algorithm and its applications to mechanical engineering: a review. Mater. Today Proc. 2, 2624–2630 (2015)CrossRefGoogle Scholar
  24. 24.
    Mirjalili, S.: Genetic algorithm. In: Studies in Computational Intelligence Series. Springer (2019)Google Scholar
  25. 25.
    Modares, H.; Alfi, A.; Fateh, M.-M.: Parameter identification of chaotic dynamic systems through an improved particle swarm optimization. Expert. Syst. Appl. 37, 3714–3720 (2010)CrossRefGoogle Scholar
  26. 26.
    Anh, K.K.; Kha, N.B.: Modeling and control of shape memory alloy actuators using Preisach model, genetic algorithm and fuzzy logic. J. Mech. Sci. Technol. 20, 636–642 (2008)Google Scholar
  27. 27.
    Consolo, G.; et al.: Removing numerical instabilities in the Preisach model identification using genetic algorithms. Phys. B Condens. Matter 372, 91–96 (2006)CrossRefGoogle Scholar

Copyright information

© King Fahd University of Petroleum & Minerals 2019

Authors and Affiliations

  1. 1.College of Engineering, Muzahimiyah BranchKing Saud UniversityRiyadhSaudi Arabia
  2. 2.LGM, ENIMUniversity of MonastirMonastirTunisia
  3. 3.College of Computer Sciences and Information, Muzahimiyah BranchKing Saud UniversityRiyadhSaudi Arabia

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