Parameter identification based on modified simulated annealing differential evolution algorithm for giant magnetostrictive actuator

There is a serious nonlinear relationship between input and output in the giant magnetostrictive actuator (GMA) and how to establish mathematical model and identify its parameters is very important to study characteristics and improve control accuracy. The current-displacement model is firstly built...

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Main Authors: Xiaohui Gao, Yongguang Liu
Format: Article
Language:English
Published: AIP Publishing LLC 2018-01-01
Series:AIP Advances
Online Access:http://dx.doi.org/10.1063/1.5009956
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spelling doaj-3dae26b8b85a4b66806706713cc848632020-11-24T21:37:56ZengAIP Publishing LLCAIP Advances2158-32262018-01-0181015002015002-910.1063/1.5009956001801ADVParameter identification based on modified simulated annealing differential evolution algorithm for giant magnetostrictive actuatorXiaohui Gao0Yongguang Liu1School of Automation Science and Electrical Engineering, Beihang University, No. 37 Xueyuan road, Haidian district, Beijing 100191, P. R. ChinaSchool of Automation Science and Electrical Engineering, Beihang University, No. 37 Xueyuan road, Haidian district, Beijing 100191, P. R. ChinaThere is a serious nonlinear relationship between input and output in the giant magnetostrictive actuator (GMA) and how to establish mathematical model and identify its parameters is very important to study characteristics and improve control accuracy. The current-displacement model is firstly built based on Jiles-Atherton (J-A) model theory, Ampere loop theorem and stress-magnetism coupling model. And then laws between unknown parameters and hysteresis loops are studied to determine the data-taking scope. The modified simulated annealing differential evolution algorithm (MSADEA) is proposed by taking full advantage of differential evolution algorithm’s fast convergence and simulated annealing algorithm’s jumping property to enhance the convergence speed and performance. Simulation and experiment results shows that this algorithm is not only simple and efficient, but also has fast convergence speed and high identification accuracy.http://dx.doi.org/10.1063/1.5009956
collection DOAJ
language English
format Article
sources DOAJ
author Xiaohui Gao
Yongguang Liu
spellingShingle Xiaohui Gao
Yongguang Liu
Parameter identification based on modified simulated annealing differential evolution algorithm for giant magnetostrictive actuator
AIP Advances
author_facet Xiaohui Gao
Yongguang Liu
author_sort Xiaohui Gao
title Parameter identification based on modified simulated annealing differential evolution algorithm for giant magnetostrictive actuator
title_short Parameter identification based on modified simulated annealing differential evolution algorithm for giant magnetostrictive actuator
title_full Parameter identification based on modified simulated annealing differential evolution algorithm for giant magnetostrictive actuator
title_fullStr Parameter identification based on modified simulated annealing differential evolution algorithm for giant magnetostrictive actuator
title_full_unstemmed Parameter identification based on modified simulated annealing differential evolution algorithm for giant magnetostrictive actuator
title_sort parameter identification based on modified simulated annealing differential evolution algorithm for giant magnetostrictive actuator
publisher AIP Publishing LLC
series AIP Advances
issn 2158-3226
publishDate 2018-01-01
description There is a serious nonlinear relationship between input and output in the giant magnetostrictive actuator (GMA) and how to establish mathematical model and identify its parameters is very important to study characteristics and improve control accuracy. The current-displacement model is firstly built based on Jiles-Atherton (J-A) model theory, Ampere loop theorem and stress-magnetism coupling model. And then laws between unknown parameters and hysteresis loops are studied to determine the data-taking scope. The modified simulated annealing differential evolution algorithm (MSADEA) is proposed by taking full advantage of differential evolution algorithm’s fast convergence and simulated annealing algorithm’s jumping property to enhance the convergence speed and performance. Simulation and experiment results shows that this algorithm is not only simple and efficient, but also has fast convergence speed and high identification accuracy.
url http://dx.doi.org/10.1063/1.5009956
work_keys_str_mv AT xiaohuigao parameteridentificationbasedonmodifiedsimulatedannealingdifferentialevolutionalgorithmforgiantmagnetostrictiveactuator
AT yongguangliu parameteridentificationbasedonmodifiedsimulatedannealingdifferentialevolutionalgorithmforgiantmagnetostrictiveactuator
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