Serious startup drift of the Ring Laser Gyroscope(RLG)is observed during cold startup process,which will dramatically degrade the performances of the corresponding Inertial Navigation System(INS).In this paper,correla...Serious startup drift of the Ring Laser Gyroscope(RLG)is observed during cold startup process,which will dramatically degrade the performances of the corresponding Inertial Navigation System(INS).In this paper,correlation analysis method,which analyzes the relationship between the startup drift of the RLG and the temperature change,is used to determine the significant temperature-related terms during gyroscope startup.Based on the significant temperature-related terms and the startup time length,a startup drift compensation model for RLG based on monotonicity-constrained Radial Basis Function(RBF)neural network is proposed and validated.Compared with the raw RLG data without compensation,the standard deviation of the RLG output with the proposed constrained RBF network model is decreased by more than 46%,and the peak-to-peak value is decreased by more than 35%.Compared with the traditional multiple regression model,the standard deviation and peak-to-peak value of the RLG output are decreased by more than 10%and 6%,respectively.Compared with the common RBF network model,the standard deviation and peak-to-peak value of the RLG output are decreased by more than 8%and 3%,respectively.Navigation experiments also validate the effectiveness of the compensation model.展开更多
基金supported in part by the National Natural Science Foundation of China(No.61203199)。
文摘Serious startup drift of the Ring Laser Gyroscope(RLG)is observed during cold startup process,which will dramatically degrade the performances of the corresponding Inertial Navigation System(INS).In this paper,correlation analysis method,which analyzes the relationship between the startup drift of the RLG and the temperature change,is used to determine the significant temperature-related terms during gyroscope startup.Based on the significant temperature-related terms and the startup time length,a startup drift compensation model for RLG based on monotonicity-constrained Radial Basis Function(RBF)neural network is proposed and validated.Compared with the raw RLG data without compensation,the standard deviation of the RLG output with the proposed constrained RBF network model is decreased by more than 46%,and the peak-to-peak value is decreased by more than 35%.Compared with the traditional multiple regression model,the standard deviation and peak-to-peak value of the RLG output are decreased by more than 10%and 6%,respectively.Compared with the common RBF network model,the standard deviation and peak-to-peak value of the RLG output are decreased by more than 8%and 3%,respectively.Navigation experiments also validate the effectiveness of the compensation model.
文摘随着汽车用皮革的迅速发展,开发一套满足汽车内饰皮革生产需求的智能切割系统具有重要意义。本文简述了汽车内饰皮革切割系统的发展,构建了基于径向基函数(Radial Basis Function,RBF)神经网络的汽车内饰皮革智能切割系统,介绍了系统主要硬件配置选型和软件的设计,提出了基于RBF神经网络PID(Proportional Integral Derivative,比例-积分-微分)控制算法;通过搭建试验平台,测试汽车内饰皮革智能切割系统的可行性、切割精度与效率。结果表明,该系统能够较好地满足汽车内饰皮革切割方面的需求。
