Automated Aerial Refueling Docking Control Based on Adaptive RBFNN Dynamic Inversion

This paper proposes a control method based on adaptive radial basis function neural networks (RBFNN) and dynamic inversion control for unmanned receiver to address the nonlinearities and uncertainties in the autonomous aerial refueling docking process. Following the principle of time-scale separation, the attitude control of the unmanned receiver is divided into fast and slow loops. A dynamic inversion method is employed to design the attitude loop controller, and an adaptive RBFNN system is designed to compensate for system model errors and external disturbances. The efficacy and stability of the designed control method were validated through simulation experiments conducted during autonomous aerial refueling docking. The results indicate that the autonomous aerial refueling docking system based on adaptive RBFNN dynamic inversion control exhibits robustness and adaptability, enabling reliable refueling operations in complex aerial environments and providing reliable support for long-duration unmanned missions.