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 bee...
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| Format: | Thesis |
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Massachusetts Institute of Technology
2024
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| Online Access: | https://hdl.handle.net/1721.1/156775 |
| Summary: | 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. |
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