摘要
在进行在轨维修以及清除等任务时,需要确定航天器的姿态四元数和角速度。失效卫星常处于自由翻滚状态,通常带有柔性帆板,其运动规律相较于刚性帆板更为复杂。一方面,空间失效卫星的姿态确定常使用激光雷达、双目相机作为测量装备,其测量精度常受到光照、磁场等的影响,会对识别精度产生较大干扰。另一方面,柔性航天器的质量特性容易发生变化,导致很难对其动力学模型进行精确描述。针对柔性自由翻滚目标的状态难以获取的问题,本文提出基于无迹卡尔曼滤波的姿态估计方法,并采用神经网络补偿柔性航天器模型误差。仿真结果显示:无损卡尔曼滤波器(Unscented Kalman filter,UKF)算法对柔性航天器的姿态四元数预测误差值在10^(-3)范围内,角速度误差值最高0.08 rad/s,采用神经网络补偿动力学模型后对四元数的预测误差稳定在9×10^(-4)范围内,角速度误差稳定在1.5×10^(-3)范围内。结果表明,使用神经网络补偿柔性航天器动力学模型的不确定项之后,UKF对柔性自由翻滚目标的姿态估计精度满足工程要求。
The attitude quaternion and angular velocity of the spacecraft need to be determined for missions such as in⁃orbit repair and removal.Failed satellites are often in a free tumbling state,usually with flexible sails,and their motion patterns are more complex than those of rigid sails.On the one hand,the attitude determination of space invalid satellites often uses LIDAR and binocular camera as measurement equipment.Its measurement accuracy is often affected by light and magnetic field,which will greatly interfere with the recognition accuracy.On the other hand,the mass characteristics of flexible spacecraft are easy to change,which makes it difficult to accurately describe its dynamic model.Aiming at the problem that it is difficult to obtain the state of the flexible free-tumbling target,an attitude estimation method based on traceless Kalman filter is proposed,and neural network is used to compensate the model error of flexible spacecraft.The simulation results show that the prediction error of attitude quaternion of the flexible spacecraft by unscented Kalman filter(UKF)algorithm is within the range of 10^(-3),and the angular velocity error is within the maximum of 0.08 rad/s,and the prediction error of quaternion is stable within the range of 9×10^(-4)and the angular velocity error is stable within the range of 1.5×10^(-3)after using neural network to compensate the dynamics model.The results show that after using neural networks to compensate for the uncertainty terms of the flexible spacecraft dynamics model,the attitude estimation accuracy of the UKF for flexible free-tumbling targets is sufficiently high.
作者
赵梓良
孙晟昕
李文龙
魏承
ZHAO Ziliang;SUN Shengxin;LI Wenlong;WEI Cheng(School of Astronautics,Harbin Institute of Technology,Harbin 150001,China;Shanghai Institute of Satellite Engineering,Shanghai 200240,China)
出处
《南京航空航天大学学报》
CAS
CSCD
北大核心
2022年第1期51-57,共7页
Journal of Nanjing University of Aeronautics & Astronautics
基金
上海航天科技创新基金(SAST2019-051)。
关键词
无迹卡尔曼滤波
在轨服务
姿态四元数
速度估计
BP神经网络
traceless Kalman filter
in-orbit service
attitude quaternion
velocity estimation
BP neural network
