A new impetus to develop autonomous aerial refueling has arisen out of the growing demand to expand the capabilities of unmanned aerial vehicles (UAVs). With autonomous aerial refueling, UAVs can retain the advantages of being small, inexpensive, and expendable, while offering superior range and loiter-time capabilities. VisNav, a vision based sensor, offers the accuracy and reliability needed in order to provide relative navigation information for autonomous probe and drogue aerial refueling for UAVs. This thesis develops a Kalman filter to be used in combination with the VisNav sensor to improve the quality of the relative navigation solution during autonomous probe and drogue refueling. The performance of the Kalman filter is examined in a closed-loop autonomous aerial refueling simulation which includes models of the receiver aircraft, VisNav sensor, Reference Observer-based Tracking Controller (ROTC), and atmospheric turbulence. The Kalman filter is tuned and evaluated for four aerial refueling scenarios which simulate docking behavior in the absence of turbulence, and with light, moderate, and severe turbulence intensity. The docking scenarios demonstrate that, for a sample rate of 100 Hz, the tuning and performance of the filter do not depend on the intensity of the turbulence, and the Kalman filter improves the relative navigation solution from VisNav by as much as 50% during the early stages of the docking maneuver. For the aerial refueling scenarios modeledin this thesis, the addition of the Kalman filter to the VisNav/ROTC structure resulted in a small improvement in the docking accuracy and precision. The Kalman filter did not, however, significantly improve the probability of a successful docking in turbulence for the simulated aerial refueling scenarios.

Automated spacecraft docking is a concept of operations with several important potential applications. One application that has received a great deal of attention recently is that of an automated docking capable unmanned re-supply spacecraft. In addition to being useful for re-supplying orbiting space stations, automated shuttles would also greatly facilitate the manned exploration of nearby space objects, including the Moon, near-Earth asteroids, or Mars. These vehicles would allow for longer duration human missions than otherwise possible and could even accelerate human colonization of other worlds. This thesis develops an optimal docking controller for an automated docking capable spacecraft. An innovative vision-based relative navigation system called VisNav is used to provide real-time relative position and orientation estimates, while a Kalman post-filter generates relative velocity and angular rate estimates from the VisNav output. The controller's performance robustness is evaluated in a closed-loop automated spacecraft docking simulation of a scenario in circular lunar orbit. The simulation uses realistic dynamical models of the two vehicles, both based on the European Automated Transfer Vehicle. A high-fidelity model of the VisNav sensor adds realism to the simulated relative navigation measurements. The docking controller's performance is evaluated in the presence of measurement noise, with the cases of sensor noise only, vehicle mass errors plus sensor noise, errors in vehicle moments of inertia plus sensor noise, initial starting position errors plus sensor noise, and initial relative attitude errors plus sensor noise each being considered. It was found that for the chosen cases and docking scenario, the final controller was robust to both types of mass property modeling errors, as well as both types of initial condition modeling errors, even in the presence of sensor noise. The VisNav system was found to perform satisfactorily in all test cases, with excellent estimate error convergence characteristics for the scenario considered. These results demonstrate preliminary feasibility of the presented docking system, including VisNav, for space-based automated docking applications.