Rudder Augmented Trajectory Correction for Unmanned Aerial Vehicles to Decrease Lateral Image Errors of Fixed Camera Payloads

This thesis developed a Rudder Augmented Trajectory Correction (RATC) method for small unmanned aerial vehicles. The goal of this type of controller is to minimize the lateral image errors of body-fixed non-gimbaled cameras. This is achieved through both aggressive trajectory following and the elimination of the roll angle present in current aileron only trajectory correction autopilots. The analytical derivation of the rudder augmented trajectory correction controller is presented. Using estimated aerodynamic derivatives of the Aerosonde UAV, RATC produced a stable and controllable system. This control algorithm was integrated into the AggieAir Minion-class UAV using the Paparazzi open source autopilot. Flight results are presented that show significant reduction in the roll angle present during trajectory correction. This is shown using both inertial measurement unit sensor data as well as payload imagery collected over a selected region of interest. The conclusion of this thesis is that the RATC algorithm is a viable solution to minimize lateral image errors for body-fixed cameras in the realm of aerial surveying.