Iterative-Learning-Based Image Servo Aerial Docking Control

In recent years, with the rapid development of unmanned aerial vehicle (UAV), aerial refueling has garnered increasing attention. During the critical docking phase of aerial refueling, traditional sensing technologies such as GPS and electro-optical systems may fail when signals are obstructed, drawing attention to vision-based solutions. Conventional image-based visual servo (IBVS) control techniques often overlook the impact of disturbances. In this paper, an iterative-learning-based IBVS (ILB-IBVS) controller is proposed, extending traditional position-based iterative learning to the 2D image plane, making the control more precise and reliable. Simulation results show that the proposed ILB-IBVS controller improves docking success rates and efficiency compared to the original IBVS controller.