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Simulated Fixed-Wing Aircraft Attitude Control using Reinforcement Learning Methods

thesis
posted on 20.12.2021, 17:59 by David Jona RichterDavid Jona Richter
Autonomous transportation is a research field that has gained huge interest in recent years, with autonomous electric or hydrogen cars coming ever closer to seeing everyday use. Not just cars are subject to autonomous research though, the field of aviation is also being explored for fully autonomous flight. One very important aspect for making autonomous flight a reality is attitude control, the control of roll, pitch, and sometimes yaw. Traditional approaches for automated attitude control use PID (proportional-integral-derivative) controllers, which use hand-tuned parameters to fulfill the task. In this work, however, the use of Reinforcement Learning algorithms for attitude control will be explored. With the surge of more and more powerful artificial neural networks, which have proven to be universally usable function approximators, Deep Reinforcement Learning also becomes an intriguing option.
A software toolkit will be developed and used to allow for the use of multiple flight simulators to train agents with Reinforcement Learning as well as Deep Reinforcement Learning. Experiments will be run using different hyperparamters, algorithms, state representations, and reward functions to explore possible options for autonomous attitude control using Reinforcement Learning.

History

Degree Type

Master of Science

Department

Technology

Campus location

Hammond

Advisor/Supervisor/Committee Chair

Ricardo A. Calix

Advisor/Supervisor/Committee co-chair

Keyuan Jiang

Additional Committee Member 2

Tae-Hoon Kim

Additional Committee Member 3

Keyuan Jiang