Cheng Jianhua, Qi Bing, Chen Daidai, Landry René
Marine Navigation Research Institute, College of Automation, Harbin Engineering University, Harbin 150001, China.
LASSENA Laboratory, École de Technologie Superieure, 1100 Notre-Dame Street West, Montreal, QC H3C 1K3, Canada.
Sensors (Basel). 2015 May 13;15(5):11189-207. doi: 10.3390/s150511189.
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.
本文提出了基于径向基函数人工神经网络（RBF ANN）的干涉式光纤陀螺仪（IFOG）温度补偿模型的改进方法。基于IFOG输出的数学表达式，提取了三个与温度相关的项，包括：（1）光纤环的温度；（2）光纤环的温度变化；（3）光纤环的温度乘积项。然后，建立了基于输入修正的RBF ANN的温度补偿方案，其中将与温度相关的项用于训练RBF ANN。进行了实验温度测试，收集了足够的数据并进行后处理以形成新型RBF ANN。最后，将基于改进的RBF ANN的温度补偿模型应用于两个具有温度补偿能力的IFOG中。实验结果表明，所提出的温度补偿模型能够有效降低环境温度对IFOG输出的影响，并且比未进行改进的传统方案具有更好的温度补偿性能。
