Deep learning based trajectory optimization for UAV aerial refueling docking under bow wave

In the autonomous aerial refueling (AAR) docking process, the bow wave generated by the receiver has a strong effect on the drogue, which affects the docking success rate greatly. Thus, a deep learning based trajectory optimization method which aims to decrease the bow wave effect on the drogue is proposed in this paper. There are mainly three parts in the proposed trajectory optimization method. Firstly, a precise bow wave model based on deep learning is presented to estimate the bow wave effect on the drogue. Furthermore, due to the dynamic characteristic of the drogue, a simple and practical drogue motion prediction model under multiple disturbances is carried out to provide a precise prediction of the drogue position at the next time. Moreover, considering the strict attitude constraints requirements in the AAR docking process, a novel reference observer is designed to estimate the receiver attitude from the optimized trajectory under wind perturbations. Then, the proposed trajectory optimization method could not only diminish the bow wave effect on the drogue largely but also satisfy the attitude constraints of the receiver. Finally, the effectiveness of the proposed method is demonstrated by the simulations.