Title: Neural Lander: Stable Drone Landing Control using Learned Dynamics,
Abstract: Precise trajectory control near ground is difficult for multi-rotor drones,
due to the complex ground 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 blends together a nominal dynamics model
coupled with a Deep Neural Network (DNN) that learns the high-order
interactions. We employ a novel application of spectral normalization to
constrain the DNN to have bounded Lipschitz behavior. 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 linear proportional-derivative (PD)
controller in both 1D and 3D landing cases. In particular, we show that
compared to the PD controller, Neural-Lander can decrease error in z direction
from 0.13m to zero, and mitigate average x and y drifts by 90% and 34%
respectively, in 1D landing. Meanwhile, Neural-Lander can decrease z error from
0.12m to zero, in 3D landing. We also empirically show that the DNN generalizes
well to new test inputs outside the training domain.