摘要
针对高速磁浮列车悬浮间隙传感器的温度漂移现象,建立了基于RBF(radial basis function)神经网络的间隙传感器温度补偿模型.通过对全局最优粒子执行梯度下降寻优,将粒子群优化算法与梯度下降算法结合得到一种寻优能力更强的混合算法,并将该方法用于RBF温度补偿模型参数优化,提高了间隙传感器的补偿精度,最后,使用现场可编程门阵列FPGA(field-programmable gate array)实现了该补偿模型并进行了实验.实验结果表明:该方法能够较好地对间隙传感器进行温度补偿,补偿后的传感器输出不受环境温度影响,全量程范围内最大误差为0.45 mm,8~12 mm工作间隙范围内误差为0.16 mm.
In order to solve the temperature drift problem of a maglev vehicle gap sensor, a temperature compensator based on RBF-NN（radial basis function neural network） was designed to compensate the temperature drift error. A hybrid algorithm was proposed to combine PSO（particle swarm optimization） algorithm with gradient descent algorithm. In the proposed algorithm,the global optimal particle of the PSO was optimized by the gradient descent method. The hybrid algorithm has stronger optimization ability. The compensation model was optimized by the hybrid algorithm and the accuracy of the compensation model was considerably improved. Finally,the compensation model was implemented in FPGA（field-programmable gate array）. Experimental results show that temperaturedrift error of the gap sensor can be compensated effectively. The compensated output of the gap sensor was independent of the temperature. The gap sensor provides correct gap data with a maximum error of 0.45 mm for full scale and a maximum error of 0. 16 mm for a working gap from 8 mm to 12 mm.
作者
靖永志
何飞
廖海军
王滢
刘国清
董金文
JING Yongzhi;HE Fei;LIAO Haijun;WANG Ying;LIU Guoqing;DONG Jinwen(Key Laboratory of Magnetic Suspension Technology and Maglev Vehicle’ Ministry of Education, Southwest Jiaotong University’ Chengdu 610031 ’ China;School of Electrical Engineering’ Southwest Jiaotong University’ Chengdu 610031, China)
出处
《西南交通大学学报》
EI
CSCD
北大核心
2018年第2期367-373,384,共8页
Journal of Southwest Jiaotong University
基金
国家自然科学基金资助项目(51377004)
中央高校基本科研业务费专项资金资助项目(2682015CX029)
关键词
磁浮列车
间隙传感器
温度补偿
RBF网络
梯度下降法
粒子群优化
maglev vehicles
gap sensor
temperature compensation
R B F network
gradient descent method
particle swarm optimization
