TY - GEN
T1 - Research on the application and compensation for startup process of FOG based on RBF neural network
AU - Shen, Jun
AU - Miao, Lingjuan
AU - Guo, Ziwei
PY - 2012
Y1 - 2012
N2 - As the core components of Fiber Optic Gyroscope (FOG) are sensitive to temperature, there is a certain temperature drift error in the working process of FOG. In particular, during the period from supplying power to achieving the nominal precision, the temperature drift of FOG is much higher. In this paper, for reducing the drift in the startup process of FOG and shortening the time of FOG startup, a scheme based on Radial Basis Function (RBF) neural networks is designed to compensate the drift in the startup process of FOG. The RBF neural network use the two inputs and single output scheme that use the temperature of FOG and the temperature change rate as the inputs and use the drift of FOG as the output. In the room temperature, the RBF neural network is used to compensate for the startup process of FOG, and the results show that the method can effectively reduce the drift and startup time of the FOG. This method is used in a certain type of FOG North Finder and can greatly reduce the North Finder preparation time and improve the north-seeking accuracy.
AB - As the core components of Fiber Optic Gyroscope (FOG) are sensitive to temperature, there is a certain temperature drift error in the working process of FOG. In particular, during the period from supplying power to achieving the nominal precision, the temperature drift of FOG is much higher. In this paper, for reducing the drift in the startup process of FOG and shortening the time of FOG startup, a scheme based on Radial Basis Function (RBF) neural networks is designed to compensate the drift in the startup process of FOG. The RBF neural network use the two inputs and single output scheme that use the temperature of FOG and the temperature change rate as the inputs and use the drift of FOG as the output. In the room temperature, the RBF neural network is used to compensate for the startup process of FOG, and the results show that the method can effectively reduce the drift and startup time of the FOG. This method is used in a certain type of FOG North Finder and can greatly reduce the North Finder preparation time and improve the north-seeking accuracy.
KW - FOG
KW - RBF neural compensation
KW - orthogonal least square (OLS)
KW - startup process
UR - http://www.scopus.com/inward/record.url?scp=84872354317&partnerID=8YFLogxK
U2 - 10.1109/WCICA.2012.6358423
DO - 10.1109/WCICA.2012.6358423
M3 - Conference contribution
AN - SCOPUS:84872354317
SN - 9781467313988
T3 - Proceedings of the World Congress on Intelligent Control and Automation (WCICA)
SP - 3195
EP - 3199
BT - WCICA 2012 - Proceedings of the 10th World Congress on Intelligent Control and Automation
T2 - 10th World Congress on Intelligent Control and Automation, WCICA 2012
Y2 - 6 July 2012 through 8 July 2012
ER -