Modification of an RBF ANN-based temperature compensation model of interferometric fiber optical gyroscopes

Research output: Contribution to journalArticleScientificpeer-review

Researchers

  • Jianhua Cheng
  • Bing Qi
  • Daidai Chen
  • René Jr Landry

Research units

  • École de technologie supérieure
  • Harbin Engineering University
  • Aalto University

Abstract

This paper presents modification of Radial Basis Function Artificial Neural Network (RBF ANN)-based temperature compensation models for Interferometric Fiber Optical Gyroscopes (IFOGs). Based on the mathematical expression of IFOG output, three temperature relevant terms are extracted, which include: (1) temperature of fiber loops; (2) temperature variation of fiber loops; (3) temperature product term of fiber loops. Then, the input-modified RBF ANN-based temperature compensation scheme is established, in which temperature relevant terms are transferred to train the RBF ANN. Experimental temperature tests are conducted and sufficient data are collected and post-processed to form the novel RBF ANN. Finally, we apply the modified RBF ANN based on temperature compensation model in two IFOGs with temperature compensation capabilities. The experimental results show the proposed temperature compensation model could efficiently reduce the influence of environment temperature on the output of IFOG, and exhibit a better temperature compensation performance than conventional scheme without proposed improvements.

Details

Original languageEnglish
Pages (from-to)11189-11207
Number of pages19
JournalSensors
Volume15
Issue number5
Publication statusPublished - 13 May 2015
MoE publication typeA1 Journal article-refereed

    Research areas

  • IFOG, MSD, RBF ANN, Temperature compensation, Temperature product term

ID: 10272003