With the open-loop fiber optic gyro (OFOG) model, output voltage and angular velocity can effectively compensate OFOG errors. However, the model cannot reflect the characteristics of OFOG errors well when it comes to pretty large dynamic angular velocities. This paper puts forward a modeling scheme with OFOG output voltage u and temperature T as the input variables and angular velocity error Δ ω as the output variable. Firstly, the angular velocity error Δ ω is extracted from OFOG output signals, and then the output voltage u , temperature T and angular velocity error Δ ω are used as the learning samples to train a Radial-Basis-Function (RBF) neural network model. Then the nonlinear mapping model over T, u and Δ ω is established and thus Δ ω can be calculated automatically to compensate OFOG errors according to T and u . The results of the experiments show that the established model can be used to compensate the nonlinear OFOG errors. The maximum, the minimum and the mean square error of OFOG angular velocity are decreased by 97.0 % , 97.1 % and 96.5 % relative to their initial values, respectively. Compared with the direct modeling of gyro angular velocity, which we researched before, the experimental results of the compensating method proposed in this paper are further reduced by 1.6 % , 1.4 % and 1.2 % , respectively, so the performance of this method is better than that of the direct modeling for gyro angular velocity.
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