摘要
高频磁致伸缩换能器的谐振状态和输出特性受电磁激励条件、发热程度以及负载状况等多种因素共同影响。为了提升高频磁致伸缩换能器的输出性能,本文深入研究了工作温度和负载变化对换能器谐振频率和最佳偏置磁场的影响规律并设计了闭环控制系统。首先,依据换能器阻抗圆图和输出加速度特性,对不同温度和负载下磁致伸缩换能器的谐振频率与最佳偏置磁场进行了实验测试以发现其变化规律;然后,基于测试数据建立了反向传播(Back Propagation,BP)神经网络预测模型并采用遗传算法和粒子群算法对BP神经网络进行优化以表征温度和负载与谐振频率和最佳偏置电流之间的非线性关系;最后,搭建了磁致伸缩换能器的闭环控制系统并利用预测模型的预测结果实时调整闭环控制器的参考值以实现谐振频率和最佳偏置磁场的自动追踪,实验结果证明了该控制系统优化换能器输出特性的有效性,不同工况下可使换能器的输出加速度幅值平均提高25.65%。
The resonance state and output characteristics of high-frequency magnetostrictive transducers are affected by various factors such as electromagnetic excitation conditions,heat generation and load conditions.In order to improve the output performance of high-frequency magnetostrictive transducers,this paper deeply studies the influence regularity with the changing of operating temperature and loads on the resonant frequency and optimal bias magnetic field of the transducer and designs a closed-loop control system.Firstly,according to the transducer impedance circle and output acceleration characteristics,the resonant frequency and the optimal bias magnetic field of the magnetostrictive transducer under different tem-peratures and loads are experimentally tested to find out their changing rules.Then,based on the test data,a BP neural network prediction model is established and optimized by genetic algorithm and particle swarm optimization algorithm to characterize the nonlinear relationship among temperature,load,resonance frequency and optimal bias current.Finally,the closed-loop control system of magnetostrictive transducer is built and the reference value of the closed-loop controller is adjusted in real time by using the prediction results of the proposed GA-BPNN prediction model to realize the automatic tracking of resonance frequency and optimal bias magnetic field.The experimental results prove that the control system is effective in optimizing the output characteristics of the transducer.The output acceleration amplitude of the transducer can be increased by an average of 25.65%under different working conditions.
作者
黄文美
张伟帅
翁玲
HUANG Wenmei;ZHANG Weishuai;WENG Ling(State Key Laboratory of Reliability and Interlligence of Electrical Equipment,Hebei University of Technology,Tianjin 300130,China;Key Laboratory of Electromagnetic Field and Electrical Apparatus Reliability of Hebei Province,Hebei University of Technology,Tianjin 300130,China)
出处
《光学精密工程》
EI
CAS
CSCD
北大核心
2022年第22期2876-2888,共13页
Optics and Precision Engineering
基金
国家自然科学基金资助项目(No.51777053,No.52077052)。
关键词
高频磁致伸缩换能器
谐振频率
偏置磁场
BP神经网络
闭环控制系统
high frequency magnetostrictive transducer
resonant frequency
bias magnetic field
BP neural network
closed-loop control system
作者简介
黄文美(1969-),女,天津人,教授,博士生导师,2005年于河北工业大学获得博士学位,研究方向为新型磁性材料与器件,电机电器及其控制。E-mail:huzwm@hebut.edu.cn;张伟帅(1997-),男,河北邯郸人,硕士研究生,2020年于河北科技大学获得学士学位,研究方向为新型磁性材料与器件,电机电器及其控制。E-mail:767555731@qq.com。
