Implementing Control-oriented Meta-learning on Hardware
Name
sohn-jcsohn-meng-eecs-2024-thesis.pdf
Description
Thesis PDF
Size
8.2 MB
Format
Adobe PDF
Checksum (MD5)
b827574872667cb1f95ce805cb043639
Author(s)
Sohn, Joshua C.
Advisor(s)
Azizan, Navid
Date Issued
May 2024
Publisher
Massachusetts Institute of Technology
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.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Terms of Use
Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0)
Copyright retained by author(s)
Persistent DSpace Link
