Comprehensive Improvement of Temperature-Dependent Jiles–Atherton Model Utilizing Variable Model Parameters

Jia, Mengfan; Yang, Liu; Ren, Ziyan; Koh, Chang-Soon

doi:10.1109/tmag.2017.2755689

Cited by 14 publications

(5 citation statements)

References 10 publications

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“…Currently, with the increasing development of artificial intelligence, some scholars also use machine learning methods to figure out the losses of soft magnetic materials [17]. The J-A model has high computational efficiency, accurate parameter description, clear physical concepts, and it can be also extended to dynamic hysteresis models for low, medium and high frequency bands [18,19]. The J-A model is currently being used in a significant capacity.…”

Section: Introductionmentioning

confidence: 99%

“…Zhang et al introduced temperature parameters into the static J-A model. At high temperatures and DC, the hysteresis characteristics of electrical steel plate can be emulated by the static J-A model that has temperature parameters [19]. Zhang et al simulated hysteresis curves of ferrite from 20 • C to 160 • C by the dynamic J-A model and analyzed variability of magnetic properties of ferrite with temperature.…”

Section: Introductionmentioning

confidence: 99%

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Hysteresis and loss characteristics of Fe-based nanocrystalline alloys based on a novel variable-temperature dynamic Jiles–Atherton hysteresis model

Xu,

Zhao,

Ren

et al. 2023

J. Phys. D: Appl. Phys.

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The magnetic characteristics of Fe-based nanocrystalline alloys can be influenced by temperature. The conventional dynamic Jiles–Atherton (J-A) hysteresis model does not take into consideration the impact that temperature has on magnetic characteristics. A novel variable-temperature dynamic J-A hysteresis model is proposed in this paper to effectively address the issue. Firstly, the hysteresis loops of Fe-based nanocrystalline are measured at −50 °C–130 °C and DC state. The five parameters of the J-A hysteresis model are identified at various temperatures using the particle swarm optimization algorithm, and five parameters are fitted as functions of temperature. Subsequently, the five parameters as functions of temperature are introduced into the conventional dynamic J-A hysteresis model to construct a novel variable-temperature dynamic J-A hysteresis model, which can not only reflect the impact of temperature but also accurately calculate the losses. Finally, hysteresis loops and losses of Fe-based nanocrystalline alloy are simulated and calculated at different temperatures and frequencies by the variable-temperature dynamic J-A hysteresis model. Meanwhile, this paper also investigates the trends and percentages of hysteresis loss, excess loss as well as eddy current loss with frequency and temperature. Compared to the results of measured data, the maximum average error of the variable-temperature dynamic J-A hysteresis model is 5.83%.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Hysteresis and loss characteristics of Fe-based nanocrystalline alloys based on a novel variable-temperature dynamic Jiles–Atherton hysteresis model

Xu,

Zhao,

Ren

et al. 2023

J. Phys. D: Appl. Phys.

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“…According to different modeling principles, hysteresis models are divided into physics‐based models and data‐driven models. The physics‐based models are constructed by considering the basic physical property of hysteresis, 18 which include such classical models as Duhem model, 19 Jiles–Atherton model, 20 Maxwell model, 21 and so forth. Instead, the data‐driven models describe hysteresis based on the measured data of PEAs without considering the physical mechanism, 22 some typical data‐driven hysteresis models include Bouc–Wen model, 23 Preisach model, 24 Prandtl–Ishlinskii model, 25,26 Krasnoselskii–Pokrovskii model, 27 fuzzy model, 28 neural‐network‐based model 29‐31 .…”

Section: Introductionmentioning

confidence: 99%

Neural network based adaptive control for a piezoelectric actuator with model uncertainty and unknown external disturbance

Chen

Sun

et al. 2022

Intl J Robust & Nonlinear

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To lessen the positioning error of the piezoelectric actuator (PEA) caused by hysteresis nonlinearity and unknown external disturbance, a neural network based adaptive controller is designed to realize the accurate trajectory tracking of the PEA. Specifically, a more universal model, consisting of a hysteresis submodel and a dynamics submodel, is first built for the PEA without the requirement of parameter identification. On this basis, a sliding mode adaptive controller capable of handling unknown parameters of the dynamics submodel is designed to weaken the damage of external disturbance to the system stability. Furthermore, to deal with the hysteresis submodel with unknown structure and parameters, a neural network based self‐tuning control scheme is developed to enable the PEA to accurately track the desired trajectory. Moreover, Lyapunov stability analysis is performed to strictly prove that the tracking error of the system can asymptotically converge to zero. Finally, the performance of the designed controller is verified via sufficient comparative simulations and experiments.

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“…Hysteresis behavior is an nonlinearity property which has been researched for many years. Many models have been utilized so as to capture the characteristic of hysteresis, like Jiles-Atherton model [7], [8], Preisach model [9], [10], Prandtl-Ishlinskii model [11], [12], Maxwell slip model [13], [14] and BW model [15], [16] are divided to physical models and mathematical models.…”

Section: Introductionmentioning

confidence: 99%

Intelligent Rate-Dependent Hysteresis Control Compensator Design With Bouc-Wen Model Based on RMSO for Piezoelectric Actuator

2020

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Piezoelectric actuators (PAs) require high precision positioning for the applications of micro electrical mechanical systems, but it exhibits hysteresis nonlinearity which deteriorates positioning accuracy if no proper compensation is given. Hysteresis nonlinear modeling of PAs is a prime choice for hysteresis compensation. This paper proposes a novel intelligent positioning control algorithm based on Bouc-Wen (BW) model for the compensation of a bi-morph type piezoelectric actuator (PA) suffering ratedependent hysteresis. A region based mixed-species swarm optimization (RMSO) algorithm is proposed for BW modeling to capture the dynamic nonlinearity of a piezoelectric actuator which exhibits rate-dependent hysteresis. Results of numerical simulations have been disclosed to illustrate the performance enhancement of RMSO over classical algorithm while they are applied to the parameter fitting problem of BW model for experimentally acquired datasets. An model based adaptive Fuzzy neural network (Fuzzy-NN) controller of PA is utilized to compensate the hysteresis for the positioning tracking control. Experimental results also illustrate the good performance of the proposed RMSO-BW based control scheme for the hysteresis compensation control of the PA.

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Comprehensive Improvement of Temperature-Dependent Jiles–Atherton Model Utilizing Variable Model Parameters

Cited by 14 publications

References 10 publications

Hysteresis and loss characteristics of Fe-based nanocrystalline alloys based on a novel variable-temperature dynamic Jiles–Atherton hysteresis model

Hysteresis and loss characteristics of Fe-based nanocrystalline alloys based on a novel variable-temperature dynamic Jiles–Atherton hysteresis model

Neural network based adaptive control for a piezoelectric actuator with model uncertainty and unknown external disturbance

Intelligent Rate-Dependent Hysteresis Control Compensator Design With Bouc-Wen Model Based on RMSO for Piezoelectric Actuator

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