Study on the Giant Magnetostrictive Materials with Actuator Displacement Model Parameter Identification

Article Preview

Abstract:

Accurate identification of model parameters is to improve the giant magnetostrictive precision displacement control key, For single algorithm is difficult to achieve for giant magnetostrictive hysteresis nonlinear model parameters accurately identify problems, in this paper, the genetic algorithm and simulated annealing algorithm fusion, First, quick search ability of genetic algorithm are used to get a better community, recycle kick ability of simulated annealing algorithm to to adjust and optimize the whole group, Presented an improved genetic simulated annealing algorithm, And its application to the giant magnetostrictive actuator displacement hysteresis nonlinear model parameter identification. The algorithm combines the advantages of genetic algorithm and simulated annealing algorithm, both the faster convergence speed, and improves the precision and quality of the optimal solution.

You might also be interested in these eBooks

Info:

Periodical:

Pages:

37-40

Citation:

Online since:

October 2013

Authors:

Export:

Price:

Permissions CCC:

Permissions PLS:

Сopyright:

© 2014 Trans Tech Publications Ltd. All Rights Reserved

Share:

Citation:

[1] VALADKHAN S, MORRIS K, KHAJEPOUR A. Reviewand comparison of hysteresis models for magnetostrictivematerials, Journal of Intelligent Material System andStructures, 2009, 20(1): pp.131-142.

DOI: 10.1177/1045389x08093563

Google Scholar

[2] JILES D C, THOELKE J B, DEVINE M K. Numericaldetermination of hysteresis parameters for the modeling of magnetic properties using the theory of ferromagnetic hysteresis, IEEE Transactions on Magnetics, 1992, 28(1): pp.27-35.

DOI: 10.1109/20.119813

Google Scholar

[3] P.Y. XU, B.T. Yang, G. Meng, etal. Modeling and parameter identification for giantmagnetostrictive actuators applied in driving segmentedmirrors, Astronomical Research and Technology, 2010, 7(2): pp.150-157.

Google Scholar

[4] J.J. Zheng, S.Y. CAO, H.L. Wang, et al. Hybrid genetic algorithms for parameter identification of a hysteresis model of magnetostrictive actuators, Neurocomputing, 2007, 70(4-6): pp.749-761.

DOI: 10.1016/j.neucom.2006.10.010

Google Scholar

[5] S.Y. CAO, B.W. Wang, J.J. Zheng, et al. Parameter identification of hysteretic model for giant magnetostrictive actuator using hybrid genetic algorithm, Proceedings of the CSEE, 2004, 24(10): pp.127-132.

Google Scholar