导航

Acta Aeronautica et Astronautica Sinica ›› 2023, Vol. 44 ›› Issue (20): 628827-628827.doi: 10.7527/S1000-6893.2023.28827

• special column • Previous Articles     Next Articles

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:2023-10-25 Published:2023-07-07
  • Contact: Jiangtao HUANG E-mail:hjtcyfx@163.com

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

CLC Number: