磁控形状记忆合金传感器特性仿真平台的构建及研究

发布时间：2018-04-27 11:51

本文选题：MSMA + 传感器　；参考：《沈阳理工大学》2017年硕士论文

【摘要】：磁控形状记忆合金(Magnetically Controlled Shape Memory Alloy简称MSMA)是近年来出现的一种新型智能材料,具有形变大、响应速度快、能量密度高等优点,在传感器领域拥有广阔的应用前景。变形的磁控形状记忆合金在外力作用下由于内部发生孪晶再取向使材料的磁化强度发生改变,通常称这种特性为MSMA逆效应。利用该特性可以研制MSMA传感器。本文基于磁控形状记忆合金的逆效应,建立磁控形状记忆合金传感器外特性模型,通过无迹卡尔曼滤波算法对磁控形状记忆合金传感器外特性模型的系数进行参数辨识,基于Matlab、Labview混合变成设计思想构建磁控形状记忆合金传感器特性仿真平台,通过该平台可以模拟不同输入条件(激振力、预压力、偏置磁场)协同作用下,获取磁控形状记忆合金传感器感应电压信号输出,同时也可以将该特性仿真平台与实验系统相结合,深化材料的实验研究进程。首先,研究MSMA的晶体结构、变形机理,而后基于材料力学、动力学、电磁学等理论建立MSMA传感器外特性模型并确立MSMA传感器感应电压与外部磁场、预压力、激振力的幅值和频率之间的函数关系式,该模型即为参数辨识的目标模型。其次,基于无迹卡尔曼滤波算法对传感器外特性模型进行参数辨识,以满足不同实验条件下模型的适用性。将无迹卡尔曼滤波算法与混沌-量子粒子群算法分别对MSMA传感器外特性模型参数辨识的结果进行比较分析,验证了无迹卡尔曼滤波算法在MSMA传感器外特性模型参数辨识方面的高效性以及准确性。最后,构建磁控形状记忆合金传感器特性仿真平台并搭建MSMA传感器实验系统,将实验结果与特性仿真平台输出结果进行比较,二者输出结果吻合。由磁控形状记忆合金传感器特性仿真平台进行感应电压信号预测,指导后续实验研究。
[Abstract]:Magnetically shaped memory alloy (Controlled Shape Memory Alloy) is a new type of smart material, which has the advantages of large deformation, fast response and high energy density. It has a broad application prospect in sensor field. The magnetization of the deformed magnetically controlled shape memory alloy changed due to the twin reorientation of the alloy under external force. This property is usually called MSMA inverse effect. Using this characteristic, MSMA sensor can be developed. Based on the inverse effect of magnetic control shape memory alloy, the external characteristic model of magnetic control shape memory alloy sensor is established in this paper. The parameters of the external characteristic model of magnetic control shape memory alloy sensor are identified by unscented Kalman filter algorithm. Based on the mixed design idea of Matlab / LabVIEW, the simulation platform for the characteristics of magnetically controlled shape memory alloy sensors is constructed. The platform can be used to simulate the synergistic action of different input conditions (excitation force, prepressure, bias magnetic field). The output of inductive voltage signal of magnetically controlled shape memory alloy sensor can be obtained. At the same time, the simulation platform of this characteristic can be combined with the experimental system to deepen the experimental research process. Firstly, the crystal structure and deformation mechanism of MSMA are studied. Then, based on the theories of material mechanics, dynamics and electromagnetism, the external characteristic model of MSMA sensor is established, and the inductive voltage, external magnetic field and prepressure of MSMA sensor are established. The function relation between amplitude and frequency of excitation force is the target model of parameter identification. Secondly, based on the unscented Kalman filter algorithm, the parameters of the sensor external characteristic model are identified to satisfy the applicability of the model under different experimental conditions. Unscented Kalman filter algorithm and chaos quantum particle swarm optimization algorithm are used to compare and analyze the parameter identification results of the external characteristic model of MSMA sensor. The efficiency and accuracy of the unscented Kalman filter algorithm in the parameter identification of the external characteristic model of MSMA sensor are verified. Finally, the simulation platform of magnetically controlled shape memory alloy sensor and the experimental system of MSMA sensor are constructed. The experimental results are compared with the output results of the characteristic simulation platform, and the two outputs are in good agreement with each other. Based on the simulation platform of magnetic control shape memory alloy sensor, the inductive voltage signal is predicted to guide the subsequent experimental research.
【学位授予单位】：沈阳理工大学
【学位级别】：硕士
【学位授予年份】：2017
【分类号】：TP212

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