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dc.contributor.advisorAzizan, Navid
dc.contributor.authorSohn, Joshua C.
dc.date.accessioned2024-09-16T13:48:21Z
dc.date.available2024-09-16T13:48:21Z
dc.date.issued2024-05
dc.date.submitted2024-07-11T14:37:17.645Z
dc.identifier.urihttps://hdl.handle.net/1721.1/156775
dc.description.abstractUnpredictable 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.
dc.publisherMassachusetts Institute of Technology
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0)
dc.rightsCopyright retained by author(s)
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/4.0/
dc.titleImplementing Control-oriented Meta-learning on Hardware
dc.typeThesis
dc.description.degreeM.Eng.
dc.contributor.departmentMassachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
mit.thesis.degreeMaster
thesis.degree.nameMaster of Engineering in Electrical Engineering and Computer Science


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