  Precise near-ground trajectory control is difficult for multi-rotor drones,
due to the complex aerodynamic effects caused by interactions between
multi-rotor airflow and the environment. Conventional control methods often
fail to properly account for these complex effects and fall short in
accomplishing smooth landing. In this paper, we present a novel
deep-learning-based robust nonlinear controller (Neural Lander) that improves
control performance of a quadrotor during landing. Our approach combines a
nominal dynamics model with a Deep Neural Network (DNN) that learns high-order
interactions. We apply spectral normalization (SN) to constrain the Lipschitz
constant of the DNN. Leveraging this Lipschitz property, we design a nonlinear
feedback linearization controller using the learned model and prove system
stability with disturbance rejection. To the best of our knowledge, this is the
first DNN-based nonlinear feedback controller with stability guarantees that
can utilize arbitrarily large neural nets. Experimental results demonstrate
that the proposed controller significantly outperforms a Baseline Nonlinear
Tracking Controller in both landing and cross-table trajectory tracking cases.
We also empirically show that the DNN generalizes well to unseen data outside
the training domain.
