Reinforcement Learning Control Design for Perching Maneuver of Unmanned Aerial Vehicles with Wind Disturbances
This paper addresses the issue of perching maneuver of unmanned aerial vehicles in wind-disturbed environments, by combining the control-oriented sparse identification of nonlinear dynamics with control (SINDYc) method and the imitation deep reinforcement learning (IDRL) control strategy. The study...
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| Format: | Article |
| Language: | zho |
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Editorial Office of Journal of Shanghai Jiao Tong University
2024-11-01
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| Series: | Shanghai Jiaotong Daxue xuebao |
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| Online Access: | https://xuebao.sjtu.edu.cn/article/2024/1006-2467/1006-2467-58-11-1753.shtml |
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| author | ZHANG Weizhen, HE Zhen, TANG Zhangfan |
| author_facet | ZHANG Weizhen, HE Zhen, TANG Zhangfan |
| author_sort | ZHANG Weizhen, HE Zhen, TANG Zhangfan |
| collection | DOAJ |
| description | This paper addresses the issue of perching maneuver of unmanned aerial vehicles in wind-disturbed environments, by combining the control-oriented sparse identification of nonlinear dynamics with control (SINDYc) method and the imitation deep reinforcement learning (IDRL) control strategy. The study focuses on the design of control strategies for perching maneuvers. First, a training environment for the perching system is established using domain randomization, which incorporates various wind conditions. Then, the SINDYc method is employed to learn sparse models of the perching system offline under different wind conditions, using historical data and a candidate function library, to effectively identify the wind information. Afterwards, the perching control strategy is trained using an IDRL algorithm within the training environment that encompasses multiple wind conditions, resulting in a control strategy for perching in wind-disturbed scenarios. Finally, numerical simulations are conducted to verify the effectiveness of the proposed perching control strategy in wind-disturbed environments. |
| format | Article |
| id | doaj-art-c57db7ef5cb442baa49d35e29977f5d3 |
| institution | Kabale University |
| issn | 1006-2467 |
| language | zho |
| publishDate | 2024-11-01 |
| publisher | Editorial Office of Journal of Shanghai Jiao Tong University |
| record_format | Article |
| series | Shanghai Jiaotong Daxue xuebao |
| spelling | doaj-art-c57db7ef5cb442baa49d35e29977f5d32024-12-03T09:54:47ZzhoEditorial Office of Journal of Shanghai Jiao Tong UniversityShanghai Jiaotong Daxue xuebao1006-24672024-11-0158111753176110.16183/j.cnki.jsjtu.2024.187Reinforcement Learning Control Design for Perching Maneuver of Unmanned Aerial Vehicles with Wind DisturbancesZHANG Weizhen, HE Zhen, TANG Zhangfan0College of Automation Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, ChinaThis paper addresses the issue of perching maneuver of unmanned aerial vehicles in wind-disturbed environments, by combining the control-oriented sparse identification of nonlinear dynamics with control (SINDYc) method and the imitation deep reinforcement learning (IDRL) control strategy. The study focuses on the design of control strategies for perching maneuvers. First, a training environment for the perching system is established using domain randomization, which incorporates various wind conditions. Then, the SINDYc method is employed to learn sparse models of the perching system offline under different wind conditions, using historical data and a candidate function library, to effectively identify the wind information. Afterwards, the perching control strategy is trained using an IDRL algorithm within the training environment that encompasses multiple wind conditions, resulting in a control strategy for perching in wind-disturbed scenarios. Finally, numerical simulations are conducted to verify the effectiveness of the proposed perching control strategy in wind-disturbed environments.https://xuebao.sjtu.edu.cn/article/2024/1006-2467/1006-2467-58-11-1753.shtmlwind disturbanceperching maneuverflight controlsparse identification of nonlinear dynamics with control (sindyc)imitation deep reinforcement learning (idrl) |
| spellingShingle | ZHANG Weizhen, HE Zhen, TANG Zhangfan Reinforcement Learning Control Design for Perching Maneuver of Unmanned Aerial Vehicles with Wind Disturbances Shanghai Jiaotong Daxue xuebao wind disturbance perching maneuver flight control sparse identification of nonlinear dynamics with control (sindyc) imitation deep reinforcement learning (idrl) |
| title | Reinforcement Learning Control Design for Perching Maneuver of Unmanned Aerial Vehicles with Wind Disturbances |
| title_full | Reinforcement Learning Control Design for Perching Maneuver of Unmanned Aerial Vehicles with Wind Disturbances |
| title_fullStr | Reinforcement Learning Control Design for Perching Maneuver of Unmanned Aerial Vehicles with Wind Disturbances |
| title_full_unstemmed | Reinforcement Learning Control Design for Perching Maneuver of Unmanned Aerial Vehicles with Wind Disturbances |
| title_short | Reinforcement Learning Control Design for Perching Maneuver of Unmanned Aerial Vehicles with Wind Disturbances |
| title_sort | reinforcement learning control design for perching maneuver of unmanned aerial vehicles with wind disturbances |
| topic | wind disturbance perching maneuver flight control sparse identification of nonlinear dynamics with control (sindyc) imitation deep reinforcement learning (idrl) |
| url | https://xuebao.sjtu.edu.cn/article/2024/1006-2467/1006-2467-58-11-1753.shtml |
| work_keys_str_mv | AT zhangweizhenhezhentangzhangfan reinforcementlearningcontroldesignforperchingmaneuverofunmannedaerialvehicleswithwinddisturbances |