Dissertation > Aviation, aerospace > Aviation > Aircraft instrumentation,avionics, flight control and navigation > Aircraft instrumentation,aerospace equipment > Gyro instrument

Control and Compensation for Opto-Electronic Platform with Fiber Optical Gyro

Author ZhangXu
Tutor HuangXianLin
School Harbin Institute of Technology
Course Navigation,Guidance and Control
Keywords opto-electronic platform FOG ARMA model Kalman filter
CLC V241.5
Type Master's thesis
Year 2010
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In the stabilizing and tracking system loop of the opto-electronic platform, the deviation with the line-of-sight(LOS) of detector occurs as a result of sensor’s output error, therefore, this paper focuses on the research on error compensation in stabilizing loop of the Optical Fiber Gyro(FOG) of the opto-electronic platform.Firstly the development of the opto-electronic platform at home and abroad is summarized. Secondly, the factors that affect the output precision of FOG are analyzed. Thirdly, the method of error analysis and system identification is introduced. Finally, the problems in the process of compensation for the opto-electronic platform are presented by this paper.Combined with the practical requirements, this system applies a platform with two frames and two stabilizing axes, uses FOG which has high precision as the sensor of each platform loop. We then calibrate some performance indexes of FOG according to angle-axis rate turntable. After that, the signal of FOG drift is analyzed through Allan variance theory. Through analysis of coefficients of each error source, we identify quantization noise and angle random walk as two important factors that affect the opto-electronic platform.To reduce the random drift of FOG, we classify and study the output signals of FOG. And we apply the typical least squares method to identify the static model of FOG, using multiple sets of data turntable measured. Moreover, we identified the random model using traditional time series analysis, which was called ARMA model.According to the established model of FOG’s input and output characteristic, our paper adopts Kalman filtering which is a model-based error compensation method. By comparing on-forward linear prediction(FLP) algorithm, using the analysis of Allan variance, and the comparison between actual output value and the results estimated by Kalman filter, we conclude that the compensation of Kalman filtering is better than FLP.Without input signal, based on PI controller, with rectangular disturbance and without rectangular disturbance, we concluded that LOS of platform detector’s the angle error was different in the actual system and the ideal FOG model. At last, the output error of FOG was compensated.

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