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Sensors 2016, 16(7), 979; doi:10.3390/s16070979

Parameters Identification of Fluxgate Magnetic Core Adopting the Biogeography-Based Optimization Algorithm

1
The Higher Educational Key Laboratory for Measuring & Control Technology and Instrumentations of Heilongjiang Province, School of Measurement-Control Technology & Communications Engineering, Harbin University of Science and Technology, Harbin 150080, China
2
School of Automation Engineering, Northeast Dianli University, Jilin 132012, China
*
Author to whom correspondence should be addressed.
Academic Editor: Vittorio M. N. Passaro
Received: 15 May 2016 / Revised: 14 June 2016 / Accepted: 17 June 2016 / Published: 25 June 2016
(This article belongs to the Section Physical Sensors)
View Full-Text   |   Download PDF [2002 KB, uploaded 25 June 2016]   |  

Abstract

The main part of the magnetic fluxgate sensor is the magnetic core, the hysteresis characteristic of which affects the performance of the sensor. When the fluxgate sensors are modelled for design purposes, an accurate model of hysteresis characteristic of the cores is necessary to achieve good agreement between modelled and experimental data. The Jiles-Atherton model is simple and can reflect the hysteresis properties of the magnetic material precisely, which makes it widely used in hysteresis modelling and simulation of ferromagnetic materials. However, in practice, it is difficult to determine the parameters accurately owing to the sensitivity of the parameters. In this paper, the Biogeography-Based Optimization (BBO) algorithm is applied to identify the Jiles-Atherton model parameters. To enhance the performances of the BBO algorithm such as global search capability, search accuracy and convergence rate, an improved Biogeography-Based Optimization (IBBO) algorithm is put forward by using Arnold map and mutation strategy of Differential Evolution (DE) algorithm. Simulation results show that IBBO algorithm is superior to Genetic Algorithm (GA), Particle Swarm Optimization (PSO) algorithm, Differential Evolution algorithm and BBO algorithm in identification accuracy and convergence rate. The IBBO algorithm is applied to identify Jiles-Atherton model parameters of selected permalloy. The simulation hysteresis loop is in high agreement with experimental data. Using permalloy as core of fluxgate probe, the simulation output is consistent with experimental output. The IBBO algorithm can identify the parameters of Jiles-Atherton model accurately, which provides a basis for the precise analysis and design of instruments and equipment with magnetic core. View Full-Text
Keywords: Jiles-Atherton model; biogeography-based optimization algorithm; Arnold map; differential evolution; fluxgate Jiles-Atherton model; biogeography-based optimization algorithm; Arnold map; differential evolution; fluxgate
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Jiang, W.; Shi, Y.; Zhao, W.; Wang, X. Parameters Identification of Fluxgate Magnetic Core Adopting the Biogeography-Based Optimization Algorithm. Sensors 2016, 16, 979.

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