  Traditional control methods are inadequate in many deployment settings
involving control of Cyber-Physical Systems (CPS). In such settings, CPS
controllers must operate and respond to unpredictable interactions, conditions,
or failure modes. Dealing with such unpredictability requires the use of
executive and cognitive control functions that allow for planning and
reasoning. Motivated by the sport of drone racing, this dissertation addresses
these concerns for state-of-the-art flight control by investigating the use of
deep neural networks to bring essential elements of higher-level cognition for
constructing low level flight controllers.
  This thesis reports on the development and release of an open source, full
solution stack for building neuro-flight controllers. This stack consists of
the methodology for constructing a multicopter digital twin for synthesize the
flight controller unique to a specific aircraft, a tuning framework for
implementing training environments (GymFC), and a firmware for the world's
first neural network supported flight controller (Neuroflight). GymFC's novel
approach fuses together the digital twinning paradigm for flight control
training to provide seamless transfer to hardware. Additionally, this thesis
examines alternative reward system functions as well as changes to the software
environment to bridge the gap between the simulation and real world deployment
environments.
  Work summarized in this thesis demonstrates that reinforcement learning is
able to be leveraged for training neural network controllers capable, not only
of maintaining stable flight, but also precision aerobatic maneuvers in real
world settings. As such, this work provides a foundation for developing the
next generation of flight control systems.
