  Trajectory tracking control for quadrotors is important for applications
ranging from surveying and inspection, to film making. However, designing and
tuning classical controllers, such as proportional-integral-derivative (PID)
controllers, to achieve high tracking precision can be time-consuming and
difficult, due to hidden dynamics and other non-idealities. The Deep Neural
Network (DNN), with its superior capability of approximating abstract,
nonlinear functions, proposes a novel approach for enhancing trajectory
tracking control. This paper presents a DNN-based algorithm as an add-on module
that improves the tracking performance of a classical feedback controller.
Given a desired trajectory, the DNNs provide a tailored reference input to the
controller based on their gained experience. The input aims to achieve a unity
map between the desired and the output trajectory. The motivation for this work
is an interactive "fly-as-you-draw" application, in which a user draws a
trajectory on a mobile device, and a quadrotor instantly flies that trajectory
with the DNN-enhanced control system. Experimental results demonstrate that the
proposed approach improves the tracking precision for user-drawn trajectories
after the DNNs are trained on selected periodic trajectories, suggesting the
method's potential in real-world applications. Tracking errors are reduced by
around 40-50% for both training and testing trajectories from users,
highlighting the DNNs' capability of generalizing knowledge.
