An RBF neural network approach towards precision motion system with selective sensor fusion

Rui Yang*, Poi Voon Er, Zidong Wang, Kok Kiong Tan

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

63 Citations (Scopus)


A radial basis function (RBF) neural network approach with a fusion of multiple signal candidates in precision motion control is studied in this paper. Sensor weightages are assigned to sensor measurements according to the selector attributes and approximated using RBF neural network in multi-sensor fusion. A specific application towards precision motion control of a linear motor system using a magnetic encoder and a soft position sensor in conjunction with an analog velocity sensor is demonstrated. Motion velocity and noise level in the sensor are chosen as the selector attributes, and the optimal sensor weightages under different attributes are approximated using RBF neural network with the reference data from laser interferometer. The experiment results illustrate that the proposed method can provide more accurate results than both single encoder measurement and existing sensor fusion methods including ordinary RBF neural network and Kalman filter based multi-sensor approach.

Original languageEnglish
Pages (from-to)31-39
Number of pages9
Publication statusPublished - 26 Jul 2016
Externally publishedYes


  • Multiple sensor
  • Position measurement
  • Precision motion system
  • RBF neural network

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