| Autor: |
Qun Ma, Yibo Wu, Muhammad Usman Shoukat, Yukai Yan, Jun Wang, Long Yang, Fuwu Yan, Lirong Yan |
| Jazyk: |
angličtina |
| Rok vydání: |
2024 |
| Předmět: |
|
| Zdroj: |
Drones, Vol 8, Iss 11, p 632 (2024) |
| Druh dokumentu: |
article |
| ISSN: |
2504-446X |
| DOI: |
10.3390/drones8110632 |
| Popis: |
Unmanned aerial vehicles (UAVs) face significant challenges in maintaining stability when subjected to external wind disturbances and internal noise. This paper addresses these issues by introducing a real-time wind speed fitting algorithm and a wind field model that accounts for varying wind conditions, such as wind shear and turbulence. To improve control in such conditions, a deep reinforcement learning (DRL) strategy is developed and tested through both simulations and real-world experiments. The results indicate a 65% reduction in trajectory tracking error with the DRL controller. Additionally, a UAV built for testing exhibited enhanced stability and reduced angular deviations in wind conditions up to level 5. These findings demonstrate the effectiveness of the proposed DRL-based control strategy in increasing UAV resilience to wind disturbances. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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