| Author Name | Affiliation | | Yunhua Wu | School of Astronautics, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China | | Nan Yang | School of Astronautics, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China | | Zhiming Chen | School of Astronautics, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China | | Bing Hua | School of Astronautics, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China |
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| Abstract: |
| On-orbit servicing, such as spacecraft maintenance, on-orbit assembly, refueling, and de-orbiting, can reduce the cost of space missions, improve the performance of spacecraft, and extend its life span. The relative state between the servicing and target spacecraft is vital for on-orbit servicing missions, especially the final approaching stage. The major challenge of this stage is that the observed features of the target are incomplete or are constantly changing due to the short distance and limited Field of View (FOV) of camera. Different from cooperative spacecraft, non-cooperative target does not have artificial feature markers. Therefore, contour features, including triangle supports of solar array, docking ring, and corner points of the spacecraft body, are used as the measuring features. To overcome the drawback of FOV limitation and imaging ambiguity of the camera, a “selfie stick” structure and a self-calibration strategy were implemented, ensuring that part of the contour features could be observed precisely when the two spacecraft approached each other. The observed features were constantly changing as the relative distance shortened. It was difficult to build a unified measurement model for different types of features, including points, line segments, and circle. Therefore, dual quaternion was implemented to model the relative dynamics and measuring features. With the consideration of state uncertainty of the target, a fuzzy adaptive strong tracking filter(FASTF) combining fuzzy logic adaptive controller (FLAC) with strong tracking filter(STF) was designed to robustly estimate the relative states between the servicing spacecraft and the target. Finally, the effectiveness of the strategy was verified by mathematical simulation. The achievement of this research provides a theoretical and technical foundation for future on-orbit servicing missions. |
| Key words: on-orbit servicing, non-cooperative spacecraft, multi-feature fusion, fuzzy adaptive filter, dual quaternion |
| DOI：10.11916/j.issn.1005-9113.19057 |
| Clc Number:V448.2 |
| Fund: |
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| Descriptions in Chinese: |
| 多特征融合的非合作航天器在轨服务相对位姿自适应估计 吴云华，杨楠，陈志明，华冰 （南京航空航天大学 航天学院，南京 210016） 创新点说明：1）以对偶四元数为基础，建立一体化的航天器相对动力学模型； 2）对于不同类型的特征，包括点、线段、圆等，很难建立统一的测量模型，因此，采用对偶四元数对测量特征进行建模； 3）在仿真过程中，考虑到服务航天器相机视场的变化，提取到的特征逐渐减少； 4）考虑到目标航天器状态的不确定性，设计一种模糊自适应强跟踪滤波器，将模糊逻辑自适应控制器与强跟踪滤波器相结合，对多重次优渐消矩阵进行“在线”自适应调整，进一步提高滤波器跟踪精度，实现对目标航天器相对状态的跟踪。 研究目的： 为提高在轨服务最后接近段视觉导航算法的精度以及稳定性。 研究方法： 1）场景分析：随着两艘航天器的距离越来越近，服务航天器的相机视场越来越小，导致观测到的特征越来越少，这使得计算两个航天器的相对位置和姿态更加困难。 2）建模方法：以对偶四元数为基础，建立一体化的航天器相对动力学模型。对于不同类型的特征，包括点、线段、圆等，采用对偶四元数对测量特征建立统一的测量模型。 3）位姿估计方法：分别采用扩展卡尔曼滤波，强跟踪滤波以及本文提出的具有模糊自适应特点的改进强跟踪卡尔曼滤波器进行对比实验，对目标航天器的位姿以及速度、角速度进行跟踪。 研究结果： 扩展卡尔曼滤波算法的跟踪能力较差，大概在30 s以后，系统才达到稳定，稳定误差也相对较大。 1）强跟踪滤波算法最突出的优点是改善了系统的快速性，跟踪能力变强，系统在1.5s就会达到稳定，稳态误差也有所降低。 2）本文提出的具有模糊自适应特点的改进强跟踪卡尔曼滤波算法相比强跟踪算法稳定时间没有发生变化，但稳态误差变小。 整体而言，本文提出的算法综合看来明显优于扩展卡尔曼滤波算法及强跟踪滤波算法的。 结论： 随着空间技术的飞速发展，在轨运行的航天器越来越多，因此有必要发展在轨服务技术。在轨服务任务的两航天器在最后接近阶段对相对状态的精度要求很高。考虑到航天器姿态和轨道的一体化，利用对偶四元数建立了精确的航天器相对动力学模型。为提高强跟踪滤波器的跟踪性能，针对强跟踪滤波器固有缺点，提出一种模糊自适应强跟踪滤波器。该方法将强跟踪滤波器中削弱因子的选取与模糊自适应控制器相结合，实现削弱因子和多个次优衰落矩阵的自适应调整，从而提高了滤波器的估计性能。 关键词：在轨服务，非合作航天器，多特征融合，模糊自适应滤波，对偶四元数 |
