Aerospace Engineering 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 …

Crazyflie 2.1 Quadcopter Nonlinear System Identification, Nhat V. Nguyen, Hope Storro, John Plimpton

2023 Symposium

Quadcopters (quad) are used widely in many industries with crucial applications such as infrastructure inspection or package delivery. The Crazyflie 2.1 quad from Bitcraze provides an excellent platform for research and development. In this project, our goal is to perform system identification on the Crazyflie to propose a complete model. A gray box method is explored, which includes leveraging the parameters that are already known, to develop a set of equations. Through theory, simulations, and measurements, a complete quadcopter model is developed.