Research on multi-physics field factors and data driven model of giant magnetostrictive actuator based on FBG sensors

P. Han, Guanlin Du
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Abstract

The Fiber Bragg Grating(FBG) sensors are applied to Giant Magnetostrictive Actuator(GMA) to obtain the multi-physics field factors, which are the basis of data driven model. The real working circumstance of GMA is complex and nonlinear, and the traditional theoretical physics model of GMA cannot satisfy it. Hence, the multi-physics field factors of the components of GMA in real working process are gathered real-time by FBG sensors, such as temperature of Giant Magnetostrictive Material(GMM) stick and coil, displacement and vibration of GMM stick, current of coil etc, which are utilized to represent the strong nonlinear characteristics of GMA. Furthermore, the data driven model of GMA is built with the Least Squares Support Vector Machine(LS-SVM) method based on multi-physics field factors. The performance of the novel GMA model is evaluated by experiment, its maximum error is 1.1% with frequency range from 0 to 1000Hz and temperature range from 20°C to 100°C.
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基于FBG传感器的超磁致伸缩作动器多物理场因素及数据驱动模型研究
将光纤光栅(FBG)传感器应用于超磁致伸缩致动器(GMA)中,获取多物理场因子,为数据驱动模型的建立奠定基础。GMA的实际工作环境是复杂的、非线性的,传统的GMA理论物理模型无法满足这种复杂的工作环境。因此,利用光纤光栅传感器实时采集GMA组件在实际工作过程中的多物理场因素,如超磁致伸缩材料(GMM)棒和线圈的温度、GMM棒的位移和振动、线圈的电流等,来表征GMA的强非线性特性。在此基础上,利用基于多物理场因素的最小二乘支持向量机(LS-SVM)方法建立了GMA的数据驱动模型。实验结果表明,该模型在0 ~ 1000Hz频率范围和20 ~ 100℃温度范围内的最大误差为1.1%。
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