Learning to fly : computational controller design for hybrid UAVs with reinforcement learning
Thesis: S.M., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2019
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| Format: | Thesis |
| Language: | eng |
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Massachusetts Institute of Technology
2019
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| Online Access: | https://hdl.handle.net/1721.1/122772 |
| _version_ | 1826207456215171072 |
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| author | Xu, Jie,S.M.Massachusetts Institute of Technology. |
| author2 | Wojciech Matusik. |
| author_facet | Wojciech Matusik. Xu, Jie,S.M.Massachusetts Institute of Technology. |
| author_sort | Xu, Jie,S.M.Massachusetts Institute of Technology. |
| collection | MIT |
| description | Thesis: S.M., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2019 |
| first_indexed | 2024-09-23T13:49:56Z |
| format | Thesis |
| id | mit-1721.1/122772 |
| institution | Massachusetts Institute of Technology |
| language | eng |
| last_indexed | 2024-09-23T13:49:56Z |
| publishDate | 2019 |
| publisher | Massachusetts Institute of Technology |
| record_format | dspace |
| spelling | mit-1721.1/1227722019-11-06T03:02:42Z Learning to fly : computational controller design for hybrid UAVs with reinforcement learning Computational controller design for hybrid UAVs with reinforcement learning Xu, Jie,S.M.Massachusetts Institute of Technology. Wojciech Matusik. Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science. Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science Electrical Engineering and Computer Science. Thesis: S.M., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2019 Cataloged from PDF version of thesis. Includes bibliographical references (pages 51-54). Hybrid unmanned aerial vehicles (UAV) combine advantages of multicopters and fixed-wing planes: vertical take-off, landing, and low energy use. However, hybrid UAVs are rarely used because controller design is challenging due to its complex, mixed dynamics. In this work, we propose a method to automate this design process by training a mode-free, model-agnostic neural network controller for hybrid UAVs. We present a neural network controller design with a novel error convolution input trained by reinforcement learning. Our controller exhibits two key features: First, it does not distinguish among flying modes, and the same controller structure can be used for copters with various dynamics. Second, our controller works for real models without any additional parameter tuning process, closing the gap between virtual simulation and real fabrication. We demonstrate the efficacy of the proposed controller both in simulation and in our custom-built hybrid UAVs. The experiments show that the controller is robust to exploit the complex dynamics when both rotors and wings are active in flight tests. by Jie Xu. S.M. S.M. Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science 2019-11-04T20:23:30Z 2019-11-04T20:23:30Z 2019 2019 Thesis https://hdl.handle.net/1721.1/122772 1125006571 eng MIT theses are protected by copyright. They may be viewed, downloaded, or printed from this source but further reproduction or distribution in any format is prohibited without written permission. http://dspace.mit.edu/handle/1721.1/7582 54 pages application/pdf Massachusetts Institute of Technology |
| spellingShingle | Electrical Engineering and Computer Science. Xu, Jie,S.M.Massachusetts Institute of Technology. Learning to fly : computational controller design for hybrid UAVs with reinforcement learning |
| title | Learning to fly : computational controller design for hybrid UAVs with reinforcement learning |
| title_full | Learning to fly : computational controller design for hybrid UAVs with reinforcement learning |
| title_fullStr | Learning to fly : computational controller design for hybrid UAVs with reinforcement learning |
| title_full_unstemmed | Learning to fly : computational controller design for hybrid UAVs with reinforcement learning |
| title_short | Learning to fly : computational controller design for hybrid UAVs with reinforcement learning |
| title_sort | learning to fly computational controller design for hybrid uavs with reinforcement learning |
| topic | Electrical Engineering and Computer Science. |
| url | https://hdl.handle.net/1721.1/122772 |
| work_keys_str_mv | AT xujiesmmassachusettsinstituteoftechnology learningtoflycomputationalcontrollerdesignforhybriduavswithreinforcementlearning AT xujiesmmassachusettsinstituteoftechnology computationalcontrollerdesignforhybriduavswithreinforcementlearning |