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航天器反作用轮扰动建模及参数辨识
引用本文:张鹏飞,程伟,王和,赵煜.航天器反作用轮扰动建模及参数辨识[J].北京航空航天大学学报,2010,36(7):879-882.
作者姓名:张鹏飞  程伟  王和  赵煜
作者单位:北京航空航天大学,航空科学与工程学院,北京,100191;中国空间技术研究院,总体部,北京,100094
基金项目:航空科学基金资助项目 
摘    要:为预测反作用轮微振动对航天器产生的影响,提出了一种改进的扰动模型.由于共振造成的扰动放大是反作用轮扰动对卫星姿态精度的最重要影响,改进模型通过引入放大系数体现结构固有频率对扰动的影响.鉴于改进模型的非线性特性,应用模拟退火遗传算法对改进模型进行了参数辨识.设计刚性六分量力测试平台对反作用轮扰动进行测试,并利用实测数据对改进模型及参数辨识结果进行验证.验证结果表明:改进模型准确地反映了反作用轮的扰动特点,模拟退火遗传算法可以提高参数辨识的精度.

关 键 词:反作用轮  建模  参数辨识  模拟退火  遗传算法
收稿时间:2009-05-31

Disturbance modeling and parameters identification of reaction wheel assembly on spacecraft
Zhang Pengfei,Cheng Wei,Wang He,Zhao Yu.Disturbance modeling and parameters identification of reaction wheel assembly on spacecraft[J].Journal of Beijing University of Aeronautics and Astronautics,2010,36(7):879-882.
Authors:Zhang Pengfei  Cheng Wei  Wang He  Zhao Yu
Institution:1. School of Aeronautic Science and Engineering, Beijing University of Aeronautics and Astronautics, Beijing 100191, China;
2. Department of Integration Engineering, China Academy of Space Technology, Beijing 100094, China
Abstract:An improved disturbance model was proposed to predict the effects of reaction wheel assembly (RWA) micro-disturbance on the spacecraft. Because the disturbance amplification by excitation of the structure modes was the most dominating effect on spacecraft attitude accuracy by RWA, the improved model expressed the effect of the structure-s nature frequency to disturbance by introducing amplification coefficients. As the nonlinear characteristic of improved model, simulated annealing genetic algorithm was used to identify the parameters of improved model. A rigid six-axis test bed was designed, and the disturbance of RWA was measured. The improved model as well as the results of parameters identification was validated by experimental data. The experimental results show that the improved model captures the disturbance characteristic precisely, and simulated annealing genetic algorithm can improve the identification accuracy.
Keywords:reaction wheel assembly  modeling  parameters identification  simulated annealing  genetic algorithms
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