飞轮扰动经验模型参数的识别与改进

对飞轮扰动模型参数的精确识别是研究光学卫星高稳定度、高分辨率成像的基础. 目前, 飞轮的扰动模型识别主要有两种: 经验模型和分析模型. 经验模型需要识别的参数是谐波级数和幅值系数, 而当前存在的参数识别算法忽略了时域截断的影响, 导致幅值系数识别存在较大偏差, 这会引起扰动响应分析的不确定性. 针对上述情况, 提出了窗函数法和与其相关的频域恢复技术, 有效弥补了时域截断的影响. 通过实验仿真结果可见, 该方法大幅度提高了幅值系数的识别精度. 

Parameter Identification and Improvement in Empirical Model of Flywheel Disturbance

It is a basic and key technology for high stability and high resolution optical satellite to identify the parameter of flywheel disturbance model accurately. There are two main disturbance models of the flywheel, i.e., empirical model and analytical model. The parameters that need to be identified in empirical model are harmonic numbers and amplitude coefficients. The effects of truncation of the time domain are neglected in traditional parameter identification method when calculating the amplitude coefficients, while the errors of the coefficients are not small sometimes, which can lead to uncertainty to the high frequency jitter response analyses. In order to improve the accuracy and reliability of the disturbance model, this paper presents windowing method and related recovery technique in frequency domain. From the result of simulation, it can be seen that this method improves the accuracy of the parameter significantly.