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Neural Lander: Stable Drone Landing Control using Learned Dynamics

Shi, Guanya and Shi, Xichen and O'Connell, Michael and Yu, Rose and Azizzadenesheli, Kamyar and Anandkumar, Animashree and Yue, Yisong and Chung, Soon-Jo (2019) Neural Lander: Stable Drone Landing Control using Learned Dynamics. In: 2019 International Conference on Robotics and Automation (ICRA). IEEE , Piscataway, NJ, pp. 9784-9790. ISBN 978-1-5386-6027-0.

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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.

Item Type:Book Section
Related URLs:
URLURL TypeDescription Paper
Shi, Guanya0000-0002-9075-3705
Shi, Xichen0000-0002-5366-9256
Yu, Rose0000-0002-8491-7937
Azizzadenesheli, Kamyar0000-0001-8507-1868
Anandkumar, Animashree0000-0002-6974-6797
Yue, Yisong0000-0001-9127-1989
Chung, Soon-Jo0000-0002-6657-3907
Additional Information:© 2019 IEEE. The authors thank Joel Burdick, Mory Gharib and Daniel Pastor Moreno. The work is funded in part by Caltech’s Center for Autonomous Systems and Technologies and Raytheon Company.
Group:GALCIT, Center for Autonomous Systems and Technologies (CAST)
Funding AgencyGrant Number
Center for Autonomous Systems and TechnologiesUNSPECIFIED
Raytheon CompanyUNSPECIFIED
Record Number:CaltechAUTHORS:20190205-100744248
Persistent URL:
Official Citation:G. Shi et al., "Neural Lander: Stable Drone Landing Control Using Learned Dynamics," 2019 International Conference on Robotics and Automation (ICRA), Montreal, QC, Canada, 2019, pp. 9784-9790. doi: 10.1109/ICRA.2019.8794351
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:92658
Deposited By: Tony Diaz
Deposited On:05 Feb 2019 19:10
Last Modified:23 Dec 2022 19:13

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