航空学报 > 2023, Vol. 44 Issue (20): 628827-628827   doi: 10.7527/S1000-6893.2023.28827

空中无人加油自主对接导航制导与控制

杜昕, 朱喆, 胡芳芳, 黄江涛(), 刘刚, 章胜, 单恩光, 唐骥罡   

  1. 中国空气动力研究与发展中心 空天技术研究所,绵阳 621000
  • 收稿日期:2023-04-06 修回日期:2023-05-05 接受日期:2023-06-17 出版日期:2023-07-10 发布日期:2023-07-07
  • 通讯作者: 黄江涛 E-mail:hjtcyfx@163.com

Guidance, navigation and control for airborne docking of autonomous aerial refueling

Xin DU, Zhe ZHU, Fangfang HU, Jiangtao HUANG(), Gang LIU, Sheng ZHANG, Enguang SHAN, Jigang TANG   

  1. Aerospace Technology Institute,China Aerodynamics Research and Development Center,Mianyang 621000,China
  • Received:2023-04-06 Revised:2023-05-05 Accepted:2023-06-17 Online:2023-07-10 Published:2023-07-07
  • Contact: Jiangtao HUANG E-mail:hjtcyfx@163.com

摘要:

空中自主加油技术能大大提高无人机的航程航时,在军用和民用上都有着十分重要的意义。受编队气动干扰、模型不确定性、头波效应、阵风干扰等因素影响,空中无人加油的对接段是所有阶段中精度要求最高和控制难度最大的一个阶段,其导航制导与控制技术是目前研究的难点和热点。本文针对无人-无人空中自主加油场景,深入研究了软管式空中加油自主对接段的鲁棒抗扰导航制导与控制技术并进行了飞行试验验证。首先,为实现受油机加速对接时高度控制不出现振荡,采用总和能量法设计纵向制导律以实现对接末段高度和速度的协调控制;其次,为提高风干扰下的对接控制精度,分别针对加油机和受油机在不同飞行阶段的轨迹跟踪特点采用L1非线性制导思想设计了横向制导律,并采用鲁棒伺服方法设计内环姿态控制律,将积分环节引入角速率控制回路提升系统型别以更好抑制外界扰动对系统的影响;然后,基于单阶段深度学习目标检测算法YOLOV4开发了锥套识别与定位算法,在强光、云雾等复杂条件下进行采样和训练以大大提升视觉系统的鲁棒性,并采用扩展卡尔曼滤波算法将图像定位信息与RTK定位信息进行融合用于相对导航;最后,设计了无人机空中加油自主对接模拟飞行试验方案,在最大程度提高对接成功率的基础上降低安全风险,并通过飞行试验验证了本文提出的鲁棒抗扰导航制导与控制方案的有效性。

关键词: 自主空中加油, 非线性制导, 鲁棒伺服控制, 视觉导航, 飞行试验

Abstract:

Autonomous aerial refueling technology can significantly improve the endurance and flight duration of unmanned aerial vehicles, and has great significance in both military and civilian applications. Due to factors such as aerodynamic interference of aircraft formation, model uncertainty, head wave effect, and gust disturbance, the docking segment of aerial unmanned refueling is the most precise and difficult stage to control, making navigation, guidance, and control technology the current research hotspots and challenges. In this paper, we study the robust anti-interference navigation, guidance, and control technology of the autonomous hose refueling docking segment in the unmanned-to-unmanned aerial refueling scenario, and conduct flight tests to confirm its effectiveness. First, to prevent oscillations during height control when the receiving aircraft is accelerating to dock, a longitudinal guidance law is designed using the total energy method to achieve coordinated control of the height and speed during the final docking stage. Second, to improve the docking control accuracy in gusty conditions, lateral guidance laws are designed based on the L1 nonlinear guidance, which considers the trajectory tracking characteristics of the tanker and receiver in different flight stages. Inner-loop attitude control laws are designed using robust servo methods, and integral terms are introduced into the angular velocity control loop to enhance system robustness and suppress the influence of external disturbances. Then, a drogue identification and positioning algorithm is developed based on the YOLOV4 single-stage deep learning object detection algorithm, which is trained and sampled under complex lighting and foggy conditions to improve the robustness of the visual system. The extended Kal-man filter algorithm is used to fuse image positioning information with RTK positioning information for relative navigation. Finally, a flight test plan for unmanned aerial refueling docking simulation is designed to maximize the success rate of docking while reducing safety risks. The flight test verifies the effectiveness of the anti-interference navigation, guidance, and control scheme proposed in this paper.

Key words: autonomous aerial refueling, nonlinear guidance, robust servo control, vision navigation, flight test

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