基于RBF神经网络和LS-SVM组合模型的磁浮车间隙传感器温度补偿

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Abstract The temperature drift mechanism of the maglev train gap sensor is analyzed and a method is proposed to solve the temperature drift problem.In this method,the combined model of the temperature inverse characteristic is designed to compensate the temperature drift error.The radial basis function neural network (RBF-NN) and the least squares support vector machine (LS-SVM) combined temperature compensation model is established with the temperature characteristic of the gap sensor.A PT1000 temperature sensor is embedded in the probe in order to provide the reference temperature.The combined model compensates the temperature drift error of the gap senor according to the temperature signal.The simulation results show that the inverse temperature characteristic can be fitted well by the combined model.The output of the compensator is independent of the tooth-groove position.The simulation studies show that this compensator can provide correct gap data with the error less than 0.14 mm in the full scale and less than 0.05 mm in the normal work gap.The precision of the combined model is better than that of any single model.The precision of the sensor is increased with this method and the compensated output of the gap sensor may meet the requirement of levitation control system.

Key words： High-speed maglev train gap sensor temperature compensation combined model RBF neural network support vector machine

Received: 08 April 2015 Published: 24 August 2016

PACS:

TM934.4

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	Jing Yongzhi
	He Fei
	Zhang Kunlun

Cite this article:

Jing Yongzhi,He Fei,Zhang Kunlun. Temperature Compensation of Maglev Train Gap Sensor Based on RBF Neural Network and LS-SVM Combined Model[J]. Transactions of China Electrotechnical Society, 2016, 31(15): 73-80.

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https://dgjsxb.ces-transaction.com/EN/ OR https://dgjsxb.ces-transaction.com/EN/Y2016/V31/I15/73

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