  In this paper, we present Asynchronous implementation of Deep Neural
Network-based Model Reference Adaptive Control (DMRAC). We evaluate this new
neuro-adaptive control architecture through flight tests on a small quadcopter.
We demonstrate that a single DMRAC controller can handle significant
nonlinearities due to severe system faults and deliberate wind disturbances
while executing high-bandwidth attitude control. We also show that the
architecture has long-term learning abilities across different flight regimes,
and can generalize to fly different flight trajectories than those on which it
was trained. These results demonstrating the efficacy of this architecture for
high bandwidth closed-loop attitude control of unstable and nonlinear robots
operating in adverse situations. To achieve these results, we designed a
software+communication architecture to ensure online real-time inference of the
deep network on a high-bandwidth computation-limited platform. We expect that
this architecture will benefit other deep learning in the closed-loop
experiments on robots.
