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.

本文提出了一种基于深度学习的鲁棒非线性控制器，提高了四旋翼降落时的控制性能，并通过实验证明了该控制器在降落和横向轨迹跟踪方面均显著优于传统的非线性控制方法。