Dynamic angular velocity modeling and error compensation of one-fiber fiber optic gyroscope (OFFOG) in the whole temperature range

Abstract

Dynamic angular velocity modeling and error compensation of VG095M in the whole temperature range, based on a radial basis function (RBF) neural network, is presented in this paper. With gyro output voltage and environmental temperature as the input and angular velocity as the output, an RBF neural network model is established. The model is trained and validated by the experiment data. The fitting error of the model is 4.3818 × 10−6 deg s−1, which shows that the model has high precision. The experiment data except the data used for modeling were processed with this model. The results show that the maximum, minimum and mean square error of the angular velocity were reduced to 4.6%, 4.3% and 4.7% respectively after compensation.