Navigation, Guidance, Control and Dynamics Commons^™

Online Aircraft System Identification Using A Novel Parameter Informed Reinforcement Learning Method, Nathan Schaff

Doctoral Dissertations and Master's Theses

This thesis presents the development and analysis of a novel method for training reinforcement learning neural networks for online aircraft system identification of multiple similar linear systems, such as all fixed wing aircraft. This approach, termed Parameter Informed Reinforcement Learning (PIRL), dictates that reinforcement learning neural networks should be trained using input and output trajectory/history data as is convention; however, the PIRL method also includes any known and relevant aircraft parameters, such as airspeed, altitude, center of gravity location and/or others. Through this, the PIRL Agent is better suited to identify novel/test-set aircraft.

First, the PIRL method is applied to …

Development And Deployment Of A Dynamic Soaring Capable Uav Using Reinforcement Learning, Jacob Adamski

Doctoral Dissertations and Master's Theses

Dynamic soaring (DS) is a bio-inspired flight maneuver in which energy can be gained by flying through regions of vertical wind gradient such as the wind shear layer. With reinforcement learning (RL), a fixed wing unmanned aerial vehicle (UAV) can be trained to perform DS maneuvers optimally for a variety of wind shear conditions. To accomplish this task, a 6-degreesof- freedom (6DoF) flight simulation environment in MATLAB and Simulink has been developed which is based upon an off-the-shelf unmanned aerobatic glider. A combination of high-fidelity Reynolds-Averaged Navier-Stokes (RANS) computational fluid dynamics (CFD) in ANSYS Fluent and low-fidelity vortex lattice (VLM) …