Implementing Control-oriented Meta-learning on Hardware

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.

Name thesis:degree_name

Master

Department dc:contributor.department

Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

2024

Author dc:creator

• Sohn, Joshua C.

Advisor dc:contributor.advisor

• Azizan, Navid

Statement dc:rights

• Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0)
• Copyright retained by author(s)

Licence dc:rights.uri

• https://creativecommons.org/licenses/by-nc-nd/4.0/

Handle dc:identifier.uri

https://hdl.handle.net/1721.1/156775

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/156775