Air to Air Refueling (AAR) was first performed over 100 years ago and until now it has almost exclusively been used in military applications. This is due to the prohibitive cost of maintaining a tanker fleet to enable refueling operations as well as the amount of training required by both tanker and receiver pilots to mitigate the risk involved with operating aircraft in close proximity. There are two methods of performing AAR operations:​ probe-drogue and flying boom. This work investigates the feasibility of converting a civilian tanker into a probe-drogue tanker for use in civilian applications. The aircraft chosen for this work is FuelBoss AT-802 as a fuel hauler. The first objective of this thesis is to model the AT-802 and explore its potential role as an AAR tanker.

The second objective of this thesis is to address the issue of risk in AAR by modeling a hose-drogue and proposing a new control technology to stabilize a drogue in flight. As no drogue system is available for experimental testing, a flexible smart structure lab workstation will be used to investigate control strategies for vibration suppression under variable system dynamics. A Deep Deterministic Policy Gradient (DDPG) algorithm is proposed in conjuncture with Domain Randomization for reinforcement training of the controller. The effectiveness of the proposed control technique and learning algorithm is verified by experimental tests, with comparison to other related control methods such as the built-in PD controller and an intelligent NF controller. Dynamic conditions of the flexible structure are simulated by placing magnetic mass blocks at different positions on the beam. Experimental results show that the proposed DDPG controller outperforms other related control methods in terms of settling time, overshoot, and mean error, without sacrificing robustness and stability. It can learn a decision-making policy in environments with large action spaces such as in vibration suppression and has potential to used for hose-drogue system control