Temperature Compensation of FOG Scale Factor Based on CPSO-BPNN
The scale factor of fiber optic gyroscope (FOG) varied with the environment temperature. This nonlinear variation seriously influences the precision of the FOG. In this article, the back propagation neural network (BPNN) based on chaos particle swarm optimization (CPSO) is used to compensate the scale factor error. It is testified by experiment, that CPSO-BPNN algorithm is an ideal method to fit the variation of scale factor with temperature, which can greatly decrease the angular rate error of FOG caused by scale factor error and guarantee the measuring precision of FOG at different temperature.
CPSO-BPNN Temperature Compensation FOG
Dunhui Zhao Jiabin Chen Yongqiang Han Chunlei Song Zhide Liu
School of Automation, Beijing Institute of Technology, Beijing, 100081, China
国际会议
The 22nd China Control and Decision Conference(2010年中国控制与决策会议)
徐州
英文
2898-2901
2010-05-26（万方平台首次上网日期，不代表论文的发表时间）