Uninhabited Aerial Vehicle (UAV) is becoming increasingly attractive in missions where human presence is undesirable or impossible. Agile maneuvers and long endurance are among the most desired advantages of UAVs over aircraft that have human pilots onboard. Past studies suggest that the performance of UAVs may be considerably improved by utilizing natural resources, especially wind energy, during flights. The key challenge of exploiting wind energy in practical UAV operations lies in the availability of reliable and timely wind field information in the operational region.

This thesis presents a practical onboard strategy that attempts to over-come this challenge, to enable UAVs in utilizing wind energy effectively during flights, and therefore to enhance performance. We propose and explore a strategy that combines wind measurement and optimal trajectory planning onboard UAVs. During a cycle of a loiter flight, a UAV can take measurements of wind velocity components over the flight region, use these measurements to estimate the local wind field through a model-based approach, and then compute a flight trajectory for the next flight cycle with the objective of optimizing fuel. As the UAV follows the planned trajectory, it continues to measure the wind components and repeats the process of updating the wind model with new estimations and planning optimal trajectories for the next flight cycle. Besides presenting an onboard trajectory planning strategy of wind energy exploration, estimation, and utilization, this research also develops a semi-analytical linearized solution to the formulated nonlinear optimal control problem.

Simulations and numerical results indicate that the fuel savings of trajectories generated using the proposed scheme depend on wind speed, wind estimation errors, rates of change in wind speed, and the wind model structures. For a given wind field, the magnitude of potential fuel savings is also contingent upon UAVs' performance capabilities.