Temperature Compensation of Maglev Vehicle Gap Sensor Based on RBF-NN Optimized by PSO

JING Yongzhi; HE Fei; LIAO Haijun; WANG Ying; LIU Guoqing; DONG Jinwen

doi:10.3969/j.issn.0258-2724.2018.02.020

Volume 31 Issue 2

Apr. 2018

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Journal of Southwest Jiaotong University > 2018 > 53(2): 367-373, 384.

JING Yongzhi, HE Fei, LIAO Haijun, WANG Ying, LIU Guoqing, DONG Jinwen. Temperature Compensation of Maglev Vehicle Gap Sensor Based on RBF-NN Optimized by PSO[J]. Journal of Southwest Jiaotong University, 2018, 53(2): 367-373, 384. doi: 10.3969/j.issn.0258-2724.2018.02.020

Citation:

JING Yongzhi, HE Fei, LIAO Haijun, WANG Ying, LIU Guoqing, DONG Jinwen. Temperature Compensation of Maglev Vehicle Gap Sensor Based on RBF-NN Optimized by PSO[J]. Journal of Southwest Jiaotong University, 2018, 53(2): 367-373, 384. doi: 10.3969/j.issn.0258-2724.2018.02.020

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Temperature Compensation of Maglev Vehicle Gap Sensor Based on RBF-NN Optimized by PSO

doi: 10.3969/j.issn.0258-2724.2018.02.020

JING Yongzhi^{1,2
,},
HE Fei^1,2,
LIAO Haijun^1,2,
WANG Ying^1,2,
LIU Guoqing^1,2,
DONG Jinwen^1,2

Received Date: 10 Jul 2016
Publish Date: 25 Apr 2018

Abstract

Abstract

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 temperature drift 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.
- maglev vehicles,
- gap sensor,
- temperature compensation,
- RBF network,
- gradient descent method,
- particle swarm optimization

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References(19)

References

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Temperature Compensation of Maglev Vehicle Gap Sensor Based on RBF-NN Optimized by PSO

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