  Recent literature in the field of machine learning (ML) control has shown
promising theoretical results for a Deep Neural Network (DNN) based Nonlinear
Adaptive Controller (DNAC) capable of achieving trajectory tracking for
nonlinear systems. Expanding on this work, this paper applies DNAC to the
Attitude Control System (ACS) of a quadrotor and shows improvement to attitude
control performance under disturbed flying conditions where the model
uncertainty is high. Moreover, these results are noteworthy for ML control
because they were achieved with no prior training data and an arbitrary system
dynamics initialization; simply put, the controller presented in this paper is
practically modelless, yet yields the ability to force trajectory tracking for
nonlinear systems while rejecting significant undesirable model disturbances
learned through a DNN. The combination of ML techniques to learn a system's
dynamics and the Lyapunov analysis required to provide stability guarantees
leads to a controller with applications in safety-critical systems that may
undergo uncertain model changes, as is the case for most aerial systems.
Experimental findings are analyzed in the final section of this paper, and DNAC
is shown to outperform the trajectory tracking capabilities of PID, MRAC, and
the recently developed Deep Model Reference Adaptive Control (DMRAC) schemes.
