MSMA传感器的建模及参数辨识研究  

Research on Modeling and Parameter Identification of MSMA Sensor

作　　者：鲁军 刘金宝 贾士杰 李万义 LU Jun;LIU Jinbao;JIA Shijie;LI Wanyi(School of Automation and Electrical Engineering,Shenyang Ligong University,Shenyang 110159)

机构地区：[1]沈阳理工大学自动化与电气工程学院,沈阳110159

出　　处：《电气工程学报》2022年第4期211-217,共7页Journal of Electrical Engineering

基　　金：国家自然科学基金(51377110);辽宁省科学技术基金(20170540774)资助项目。

摘　　要：MSMA传感器是一种将机械力信号转化为感应电压信号的新型传感器,为了更好地实现MSMA传感器的应用,需要对其进行建模研究。由于传感器输入和输出之间呈现非线性关系,会增加建模的复杂性,并且需要辨识的未知参数较多。采用机理法结合电磁学理论建立了MSMA传感器的数学模型,并采用改进的粒子群算法对MSMA传感器数学模型的未知参数进行辨识。在辨识过程中,引入调节系数,对粒子的权重和最大速度进行动态调整,加快了算法的收敛速度,对社会学习因子进行改进,增强了算法的全局搜索能力。最后,将传感器数学模型计算的感应电压值与试验得到的感应电压值对比,验证了模型的正确性,也表明了该算法可以有效解决MSMA传感器数学模型的辨识问题。MSMA sensor is a new type of sensor that converts mechanical force signal into induced voltage signal.In order to better realize the application of MSMA sensor,it is necessary to model it.Due to the nonlinear relationship between the input and output of the sensor,it will increase the complexity of modeling,and there are many unknown parameters to be identified.The mathematical model of MSMA sensor is established by mechanism method combined with electromagnetic theory,and the unknown parameters of MSMA sensor mathematical model are identified by improved particle swarm optimization algorithm.In the identification process,the adjustment coefficient is introduced to dynamically adjust the weight and maximum speed of the particles,speed up the convergence of the algorithm,improve the social learning factor,and enhance the global search ability of the algorithm.Finally,the induced voltage calculated by the sensor mathematical model is compared with the induced voltage obtained by the test,which verifies the correctness of the model,and also shows that the algorithm can effectively solve the identification problem of the MSMA sensor mathematical model.

关 键 词：MSMA传感器 建模 改进粒子群算法 参数辨识 

分 类 号：TP359[自动化与计算机技术—计算机系统结构]