Compensation of gap sensor for high-speed maglev train with RBF neural network

Lianqing Liu, Hiroyasu Iwata, Yongzhi Jing, Jian Xiao, Kunlun Zhang

Research output: Contribution to journalArticlepeer-review

8 Citations (Scopus)


The gap sensor plays an important role for a electro-magnetic levitation system, which is a critical component of high-speed maglev trains. An artificial neural network is a promising area in the development of intelligent sensors. In this paper, a radial basis function (RBF) neural network modelling approach is introduced for the compensation of the non-contact inductive gap sensor of the high-speed maglev train. As an inverse model compensator, the designed RBF-based model is connected in series to the output terminal of the gap sensor. The network is trained by using a gradient descent learning algorithm with momentum. This scheme could estimate accurately the correct air-gap distance in a wide range of temperatures. The simulation studies of this model show that it can provide a compensated gap value with an error of less than ±0.4 mm at any temperature from 20° to 80°C. In particulr, the maximum estimation error can be reduced to ±0.1 mm when the working gap varies from 8 to 12 mm. The experimental results indicate that the compensated gap signal could meet the requirements of the levitation control system.

Original languageEnglish
Pages (from-to)933-939
Number of pages7
JournalTransactions of the Institute of Measurement and Control
Issue number7
Publication statusPublished - 2013 Oct
Externally publishedYes


  • Air gap
  • high-speed maglev train
  • inductive sensor
  • neural network
  • radial basis function

ASJC Scopus subject areas

  • Instrumentation


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