Research on PID Parameter Tuning and Optimization Based on SAC-Auto for USV Path Following
Unmanned surface vessels (USVs) are required to follow a path during a task. This is essential for the USV, especially when following a curvilinear path or considering the interference of waves, and this work has been proven to be complicated. In this paper, a PID parameter tuning and optimizing met...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-12-01
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| Series: | Journal of Marine Science and Engineering |
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| Online Access: | https://www.mdpi.com/2077-1312/10/12/1847 |
| _version_ | 1797456937813540864 |
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| author | Lifei Song Chuanyi Xu Le Hao Jianxi Yao Rong Guo |
| author_facet | Lifei Song Chuanyi Xu Le Hao Jianxi Yao Rong Guo |
| author_sort | Lifei Song |
| collection | DOAJ |
| description | Unmanned surface vessels (USVs) are required to follow a path during a task. This is essential for the USV, especially when following a curvilinear path or considering the interference of waves, and this work has been proven to be complicated. In this paper, a PID parameter tuning and optimizing method based on deep reinforcement learning were proposed to control the USV heading. Firstly, the Abkowite dynamics model with three degrees of freedom (DOF) is established. Secondly, the guidance law on the line-of-sight (LOS) method and the USV heading control system of the PID controller are designed. To satisfy the time-varying demand of PID parameters for guiding control, especially when the USV moves in waves, the soft actor–critic auto (SAC-auto) method is presented to adjust the PID parameters automatically. Thirdly, the state, action, and reward functions of the agent are designed for training and learning. Finally, numerical simulations are performed, and the results validated the feasibility and validity of the feasibility and effectiveness of the proposed method. |
| first_indexed | 2024-03-09T16:15:02Z |
| format | Article |
| id | doaj.art-8d60148136844b9c81389c3334d16123 |
| institution | Directory Open Access Journal |
| issn | 2077-1312 |
| language | English |
| last_indexed | 2024-03-09T16:15:02Z |
| publishDate | 2022-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Journal of Marine Science and Engineering |
| spelling | doaj.art-8d60148136844b9c81389c3334d161232023-11-24T15:55:21ZengMDPI AGJournal of Marine Science and Engineering2077-13122022-12-011012184710.3390/jmse10121847Research on PID Parameter Tuning and Optimization Based on SAC-Auto for USV Path FollowingLifei Song0Chuanyi Xu1Le Hao2Jianxi Yao3Rong Guo4Key Laboratory of High Performance Ship Technology, Wuhan University of Technology, Ministry of Education, Wuhan 430070, ChinaKey Laboratory of High Performance Ship Technology, Wuhan University of Technology, Ministry of Education, Wuhan 430070, ChinaKey Laboratory of High Performance Ship Technology, Wuhan University of Technology, Ministry of Education, Wuhan 430070, ChinaKey Laboratory of High Performance Ship Technology, Wuhan University of Technology, Ministry of Education, Wuhan 430070, ChinaKey Laboratory of High Performance Ship Technology, Wuhan University of Technology, Ministry of Education, Wuhan 430070, ChinaUnmanned surface vessels (USVs) are required to follow a path during a task. This is essential for the USV, especially when following a curvilinear path or considering the interference of waves, and this work has been proven to be complicated. In this paper, a PID parameter tuning and optimizing method based on deep reinforcement learning were proposed to control the USV heading. Firstly, the Abkowite dynamics model with three degrees of freedom (DOF) is established. Secondly, the guidance law on the line-of-sight (LOS) method and the USV heading control system of the PID controller are designed. To satisfy the time-varying demand of PID parameters for guiding control, especially when the USV moves in waves, the soft actor–critic auto (SAC-auto) method is presented to adjust the PID parameters automatically. Thirdly, the state, action, and reward functions of the agent are designed for training and learning. Finally, numerical simulations are performed, and the results validated the feasibility and validity of the feasibility and effectiveness of the proposed method.https://www.mdpi.com/2077-1312/10/12/1847unmanned surface vehicledeep reinforcement learningparameter tuningpath-following control |
| spellingShingle | Lifei Song Chuanyi Xu Le Hao Jianxi Yao Rong Guo Research on PID Parameter Tuning and Optimization Based on SAC-Auto for USV Path Following Journal of Marine Science and Engineering unmanned surface vehicle deep reinforcement learning parameter tuning path-following control |
| title | Research on PID Parameter Tuning and Optimization Based on SAC-Auto for USV Path Following |
| title_full | Research on PID Parameter Tuning and Optimization Based on SAC-Auto for USV Path Following |
| title_fullStr | Research on PID Parameter Tuning and Optimization Based on SAC-Auto for USV Path Following |
| title_full_unstemmed | Research on PID Parameter Tuning and Optimization Based on SAC-Auto for USV Path Following |
| title_short | Research on PID Parameter Tuning and Optimization Based on SAC-Auto for USV Path Following |
| title_sort | research on pid parameter tuning and optimization based on sac auto for usv path following |
| topic | unmanned surface vehicle deep reinforcement learning parameter tuning path-following control |
| url | https://www.mdpi.com/2077-1312/10/12/1847 |
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