{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/neural-lander-stable-drone-landing-control","title":"Neural Lander: Stable Drone Landing Control using Learned Dynamics","arxiv_id":"1811.08027","date":"2018-11-19","proceeding":null,"authors":["Guanya Shi","Xichen Shi","Michael O'Connell","Rose Yu","Kamyar Azizzadenesheli","Animashree Anandkumar","Yisong Yue","Soon-Jo Chung"],"abstract":"Precise near-ground trajectory control is difficult for multi-rotor drones,\ndue to the complex aerodynamic effects caused by interactions between\nmulti-rotor airflow and the environment. Conventional control methods often\nfail to properly account for these complex effects and fall short in\naccomplishing smooth landing. In this paper, we present a novel\ndeep-learning-based robust nonlinear controller (Neural Lander) that improves\ncontrol performance of a quadrotor during landing. Our approach combines a\nnominal dynamics model with a Deep Neural Network (DNN) that learns high-order\ninteractions. We apply spectral normalization (SN) to constrain the Lipschitz\nconstant of the DNN. Leveraging this Lipschitz property, we design a nonlinear\nfeedback linearization controller using the learned model and prove system\nstability with disturbance rejection. To the best of our knowledge, this is the\nfirst DNN-based nonlinear feedback controller with stability guarantees that\ncan utilize arbitrarily large neural nets. Experimental results demonstrate\nthat the proposed controller significantly outperforms a Baseline Nonlinear\nTracking Controller in both landing and cross-table trajectory tracking cases.\nWe also empirically show that the DNN generalizes well to unseen data outside\nthe training domain.","url_abs":"http://arxiv.org/abs/1811.08027v2","url_pdf":"http://arxiv.org/pdf/1811.08027v2.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"neural-lander-stable-drone-landing-control","repo_url":"https://github.com/JacopoPan/gym-pybullet-drones","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"unanswered"}},{"paper_slug":"neural-lander-stable-drone-landing-control","repo_url":"https://github.com/utiasDSL/gym-pybullet-drones","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"unanswered"}}],"tasks":[],"methods":[{"method_slug":"spectral-normalization","method_name":"Spectral Normalization"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1811.08027","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}