Abstract
As an important angle sensor of the opto-electric platform, gyro output accuracy plays a vital role in the stabilization and track accuracy of the whole system. It is known that the generally used fixed-bandwidth filters, single neural network models, or linear models cannot compensate for gyro error well, and so they cannot meet engineering needs satisfactorily. In this paper, a novel hybrid ARIMA-Elman model is proposed. For the reason that it can fully combine the strong linear approximation capability of the ARIMA model and the superior nonlinear compensation capability of a neural network, the proposed model is suitable for handling gyro error, especially for its non-stationary random component. Then, to solve the problem that the parameters of ARIMA model and the initial weights of the Elman neural network are difficult to determine, a differential algorithm is initially utilized for parameter selection. Compared with other commonly used optimization algorithms (e.g., the traditional least-squares identification method and the genetic algorithm method), the intelligence differential algorithm can overcome the shortcomings of premature convergence and has higher optimization speed and accuracy. In addition, the drift error is obtained based on the technique of lift-wavelet separation and reconstruction, and, in order to weaken the randomness of the data sequence, an ashing operation and Jarque-Bear test have been added to the handle process. In this study, actual gyro data is collected and the experimental results show that the proposed method has higher compensation accuracy and faster network convergence, when compared with other commonly used error-compensation methods. Finally, the hybrid method is used to compensate for gyro error collected in other states. The test results illustrate that the proposed algorithm can effectively improve error compensation accuracy, and has good generalization performance.
Highlights
Fiber-optic gyroscopes [1,2,3] have been widely used due to their high precision, short starting time, strong impact resistance, and large dynamic range
Gyro error compensation techniques have attracted the attention of many experts for a long time
In order to weaken the randomness of the data sequence, an ashing technique was operated and drift error separated into low-volatility and high-volatility components
Summary
Fiber-optic gyroscopes (gyros) [1,2,3] have been widely used due to their high precision, short starting time, strong impact resistance, and large dynamic range. Using a neural network to establish a fiber-optic gyroscope temperature drift model can achieve good approximation results. There is literature which uses a reverse transmission (BP) neural network and a mathematical statistics method to establish the drift error compensation model of the gyroscope. Some studies [6,7] selected a radial basis (RBF) neural network to identify the temperature drift of the fiber-optic gyroscope. The comparison of the simulation results verifies the superiority of GRBFN, which improves the convergence speed greatly, and has a good modeling effect Whether it is a BP neural network or a RBF neural network, only three basic network layers are included and the modeling effect on time-varying signals is not very satisfactory. In this paper, the random gyro drift error model is studied by use of an Elman neural network.
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