Abstract
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 and temperature as the input variables and angular velocity error as the output variable. Firstly, the angular velocity error is extracted from OFOG output signals, and then the output voltage , temperature and angular velocity error are used as the learning samples to train a Radial-Basis-Function (RBF) neural network model. Then the nonlinear mapping model over T, and is established and thus can be calculated automatically to compensate OFOG errors according to and . 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 , and 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 , and , respectively, so the performance of this method is better than that of the direct modeling for gyro angular velocity.
Highlights
Acting as the angular rate sensor, the fiber optic gyro (FOG) is widely applied in navigation and weapon systems [1,2,3,4]
The angular velocity error Δω is extracted from the open-loop fiber optic gyro (OFOG) output signals, and output voltage u, temperature T and angular velocity error Δω are used as the learning samples to train a Radial-Basis-Function (RBF) neural network model
The flow of the gyro angular velocity error compensation is shown in Figure 4: (1) the gyro output voltage u and temperature T are collected; (2) we make use of Equation (2) to calculate the nominal angular velocity; (3) the gyro output voltage u and temperature T are sent to the RBF neural network, which figures out the angular velocity error Δω ; (4) Equation (3) is utilized to calculate the angular velocity ω z and estimate the real value of angular velocity precisely
Summary
Acting as the angular rate sensor, the fiber optic gyro (FOG) is widely applied in navigation and weapon systems [1,2,3,4]. The nonlinearity of scale factors of the close-loop fiber optic gyro (CFOG) is limited using the homodyne detection technology, while the drift of CFOG is the main error which has an impact on its performance [5,6,7]. Under the comprehensive effects of temperature and angular velocity, the OFOG drift is relatively small and the nonlinearity of scale factors is the main error that restricts the accuracy of its applications [12,13]. To deal with the problem, this paper expands upon the studies in [3], putting forward a modeling scheme with OFOG output voltage u and temperature T as the input variables and angular velocity error Δω as the output variable. The model put forward in this paper is validated and tested experimentally, and the results are analyzed in comparison with those in [3]
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