Research on Improved Method of Jiles-Atherton Hysteresis Model Based on Parameter Identification of Genetic Algorithm

Author(s):  
Kangkang Han ◽  
Zhiguo Hao ◽  
Zhiyuan Liu ◽  
Xiaojun Yu ◽  
Hongxiang Xu
2021 ◽  
Author(s):  
Yinguo Yang ◽  
Liling Xiang ◽  
Yitan Guo ◽  
Zhendong Tan ◽  
Yankan Song

2014 ◽  
Vol 2014 ◽  
pp. 1-14 ◽  
Author(s):  
Haichen Qin ◽  
Ningbin Bu ◽  
Wei Chen ◽  
Zhouping Yin

Hysteresis behaviour degrades the positioning accuracy of PZT actuator for ultrahigh-precision positioning applications. In this paper, a corrected hysteresis model based on Bouc-Wen model for modelling the asymmetric hysteresis behaviour of PZT actuator is established by introducing an input biasφand an asymmetric factorΔΦinto the standard Bouc-Wen hysteresis model. A modified particle swarm optimization (MPSO) algorithm is established and realized to identify and optimize the model parameters. Feasibility and effectiveness of MPSO are proved by experiment and numerical simulation. The research results show that the corrected hysteresis model can represent the asymmetric hysteresis behaviour of the PZT actuator more accurately than the noncorrected hysteresis model based on the Bouc-Wen model. The MPSO parameter identification method can effectively identify the parameters of the corrected and noncorrected hysteresis models. Some cases demonstrate the corrected hysteresis model and the MPSO parameter identification method can be used to model smart materials and structure systems with the asymmetric hysteresis behaviour.


IEEE Access ◽  
2017 ◽  
Vol 5 ◽  
pp. 3131-3138 ◽  
Author(s):  
Bernard B. Munyazikwiye ◽  
Hamid Reza Karimi ◽  
Kjell G. Robbersmyr

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