Vision-based trajectory tracking controller for autonomous close proximity operations

Tight unmanned aerial vehicle (UAV) autonomous missions such as formation flight and aerial refueling (AR) requires an active controller that works in conjunction with a precise vision-based sensor that is able to extract In-front aircraft relative position and orientation from captured images. A key point in implementing such a sensor is its robustness in the presence of noises and other uncertainties. In this paper, a new vision-based algorithm that uses neural networks to estimate the In-front aircraft relative orientation and position is developed. The accuracy and robustness of the proposed algorithm has been validated via a detailed modeling and a complete virtual environment based on the 6DOF nonlinear simulation of aircraft dynamics in an autonomous aerial refueling (AAR) mission. In addition, a tracking controller based on linear methods is designed to generate required command for aircraft control surfaces and engine during an AAR. The performance of the system in the presence of noise and disturbances has also been examined. The obtain results are quite satisfactory.