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作 者:张风策 雷军刚[1] 王佐磊 敏健 李云鹏[1] 张文艳 ZHANG Fengce;LEI Jungang;WANG Zuolei;MIN Jian;LI Yunpeng;ZHANG Wenyan(Lanzhou Institute of Physics,Lanzhou 730000,China)
出 处:《真空与低温》2023年第4期406-413,共8页Vacuum and Cryogenics
基 金:国家重点研发计划(2021YFC2202401)。
摘 要:静电悬浮加速度计在运行过程中受到温度的影响,会在低频带与被测加速度信号耦合,导致输出产生漂移。为进一步提高其测量精度,提出了一种基于神经网络的温漂补偿方法。模拟静电悬浮加速度计实际在轨环境条件,在热真空罐中进行温控实验,采集测温点温度及其相对应的加速度计位移检测和反馈电压数据。利用BP神经网络的非线性映射能力,建立了关于静电悬浮加速度计的测温点温度与温漂影响的补偿模型。根据静电悬浮加速度计采集到的测温点温度数据,直接预测得到六个输出通道的温漂影响数据。实验结果表明,利用所提出的神经网络模型进行温漂补偿,能有效抑制静电悬浮加速度计的温度漂移。The electrostatic suspended accelerometer is affected by temperature during operation which can couple with the measured acceleration signal in the low frequency band,resulting in output drift.To further improve its measurement accuracy,a temperature drift compensation method based on neural network is proposed.In order to simulate the actual on-orbit environmental conditions of an electrostatic suspended accelerometer,a temperature control experiment was conducted in a thermal vacuum tank to collect the temperature of the temperature measurement point,as well as the corresponding accelerometer displacement detection and feedback voltage data.By utilizing the nonlinear mapping ability of BP neural network,a compensation model was established for the temperature measurement point temperature of the electrostatic suspended accelerometer and the impact of the electrostatic suspended accelerometer temperature drift.The magnitude of the temperature drift effect of the six output channels is directly predicted from the temperature data of the temperature measurement points collected by the electrostatic suspended accelerometer.The experimental results show that the temperature drift of the electrostatic suspended accelerometer can be effectively suppressed after using the proposed neural network model for temperature drift compensation.
分 类 号:TB942[一般工业技术—计量学]
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