Neural Lander: Stable Drone Landing Control Using Learned Dynamics

• Published in: Proceedings - IEEE International Conference on Robotics and Automation pp. 9784 - 9790
• Main Authors: Shi, Guanya, Shi, Xichen, O'Connell, Michael, Yu, Rose, Azizzadenesheli, Kamyar, Anandkumar, Animashree, Yue, Yisong, Chung, Soon-Jo
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.05.2019
• Subjects: Aerodynamics; Neural networks; Rotors; Stability analysis; Training; Trajectory tracking; Vehicle dynamics
• ISSN: 2577-087X
• DOI: 10.1109/ICRA.2019.8794351

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.