2026-09-19T02:34:03Zhttps://dspace.mit.edu/server/oai/requestoai:dspace.mit.edu:1721.1/1567752024-09-17T03:33:02Zcom_1721.1_7582com_1721.1_7581col_1721.1_131023
Azizan, Navid
Sohn, Joshua C.
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
2024-09-16T13:48:21Z
2024-09-16T13:48:21Z
2024-05
2024-07-11T14:37:17.645Z
https://hdl.handle.net/1721.1/156775
Unpredictable weather conditions pose a daunting challenge for the robust control of unmanned aerial vehicles, also known as drones. The control-oriented meta-learning algorithm aims to solve this problem by learning a controller that can adapt to dynamic environments. This algorithm has already been derived and simulated for a two-dimensional model. This project explores the implementation of the control-oriented meta-learning algorithm on a hardware platform. After extending the algorithm to a three-dimensional model, it was tested in a physics-based simulator and deployed on a hexarotor in the real world. Both in simulation and in real life, the learned controller outperformed a traditional controller in the presence of wind.
M.Eng.
Massachusetts Institute of Technology
Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0)
Copyright retained by author(s)
https://creativecommons.org/licenses/by-nc-nd/4.0/
Implementing Control-oriented Meta-learning on Hardware
Thesis
application/pdf
Master
Master of Engineering in Electrical Engineering and Computer Science
Publication
unknown
unknown
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<Abstract>Unpredictable weather conditions pose a daunting challenge for the robust control of unmanned aerial vehicles, also known as drones. The control-oriented meta-learning algorithm aims to solve this problem by learning a controller that can adapt to dynamic environments. This algorithm has already been derived and simulated for a two-dimensional model. This project explores the implementation of the control-oriented meta-learning algorithm on a hardware platform. After extending the algorithm to a three-dimensional model, it was tested in a physics-based simulator and deployed on a hexarotor in the real world. Both in simulation and in real life, the learned controller outperformed a traditional controller in the presence of wind.</Abstract>
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