Single-Shot Learning of Multirotor Controller Gains: A Data-Driven Approach with Experimental Validation
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arXiv
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| Autori principali: | , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866908397006225408 |
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| author | Mirtaba, Mohammad Oveissi, Parham Salaza, Juan Augusto Paredes Goel, Ankit |
| author_facet | Mirtaba, Mohammad Oveissi, Parham Salaza, Juan Augusto Paredes Goel, Ankit |
| contents | This paper demonstrates the single-shot learning capabilities of retrospective cost optimization based data-driven control applied to learning multirotor controller gains for trajectory tracking. In particular, the proposed control approach is first used within a simple multirotor simulation environment to learn appropriate multirotor controller gains to follow a trajectory. Then, the gains resulting from a single simulation run are used in a more complex multirotor simulation environment based on Simulink for performance verification. Finally, the resulting gains are implemented in a physical quadrotor and the results for waypoint and trajectory tracking are reported in this paper. The proposed control approach is the continuous-time version of the widely used discrete-time retrospective control adaptive control algorithm, which is simpler to implement within continuous-time simulation environments and whose performance does not depend on appropriate sampling time choice. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_03815 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Single-Shot Learning of Multirotor Controller Gains: A Data-Driven Approach with Experimental Validation Mirtaba, Mohammad Oveissi, Parham Salaza, Juan Augusto Paredes Goel, Ankit Systems and Control This paper demonstrates the single-shot learning capabilities of retrospective cost optimization based data-driven control applied to learning multirotor controller gains for trajectory tracking. In particular, the proposed control approach is first used within a simple multirotor simulation environment to learn appropriate multirotor controller gains to follow a trajectory. Then, the gains resulting from a single simulation run are used in a more complex multirotor simulation environment based on Simulink for performance verification. Finally, the resulting gains are implemented in a physical quadrotor and the results for waypoint and trajectory tracking are reported in this paper. The proposed control approach is the continuous-time version of the widely used discrete-time retrospective control adaptive control algorithm, which is simpler to implement within continuous-time simulation environments and whose performance does not depend on appropriate sampling time choice. |
| title | Single-Shot Learning of Multirotor Controller Gains: A Data-Driven Approach with Experimental Validation |
| topic | Systems and Control |
| url | https://arxiv.org/abs/2410.03815 |