dc.contributor.advisor | Azizan, Navid | |
dc.contributor.author | Sohn, Joshua C. | |
dc.date.accessioned | 2024-09-16T13:48:21Z | |
dc.date.available | 2024-09-16T13:48:21Z | |
dc.date.issued | 2024-05 | |
dc.date.submitted | 2024-07-11T14:37:17.645Z | |
dc.identifier.uri | https://hdl.handle.net/1721.1/156775 | |
dc.description.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. | |
dc.publisher | Massachusetts Institute of Technology | |
dc.rights | Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0) | |
dc.rights | Copyright retained by author(s) | |
dc.rights.uri | https://creativecommons.org/licenses/by-nc-nd/4.0/ | |
dc.title | Implementing Control-oriented Meta-learning on Hardware | |
dc.type | Thesis | |
dc.description.degree | M.Eng. | |
dc.contributor.department | Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science | |
mit.thesis.degree | Master | |
thesis.degree.name | Master of Engineering in Electrical Engineering and Computer Science | |