Optimization Method of Underwater Flapping Foil Propulsion Performance Based on Gaussian Process Regression and Deep Reinforcement Learning
In order to overcome the complexity and variability of underwater working environments, as well as the difficulty of controlling the flapping motion due to the significant nonlinear characteristics and numerous variables involved, a direct exploration approach is proposed to search for the optimal f...
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Editorial Office of Journal of Shanghai Jiao Tong University
2025-01-01
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| Series: | Shanghai Jiaotong Daxue xuebao |
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| Online Access: | https://xuebao.sjtu.edu.cn/article/2025/1006-2467/1006-2467-59-1-70.shtml |
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| author | YANG Yinghe, WEI Handi, FAN Dixia, LI Ang |
| author_facet | YANG Yinghe, WEI Handi, FAN Dixia, LI Ang |
| author_sort | YANG Yinghe, WEI Handi, FAN Dixia, LI Ang |
| collection | DOAJ |
| description | In order to overcome the complexity and variability of underwater working environments, as well as the difficulty of controlling the flapping motion due to the significant nonlinear characteristics and numerous variables involved, a direct exploration approach is proposed to search for the optimal flapping foil propulsion parameters in the environment. The Latin hypercube sampling technique is utilized to obtain the samples of multi-dimensional flapping parameters in actual water pool data, and a Gaussian process regression (GPR) machine learning model is established based on these samples to generalize the working environment. Under different propulsion performance requirements, the TD3 algorithm in deep reinforcement learning (DRL) is trained for maximizing rewards and obtaining the optimal combination of multiple parameter actions in continuous intervals. The experimental results demonstrate that the GPR-TD3 method is capable of learning the globally optimal solution for flapping propulsion in the experimental environment, including maximum speed and maximum efficiency. Furthermore, the accuracy of this optimal solution can be intuitively verified through a two-dimensional contour plot in the GPR. Meanwhile, with 290 sets of real samples provided for any given propulsion speed requirement, the agent can recommend a set of action combinations with an error range of 0.23% to 6.68%, which can provide reference for practical applications. |
| format | Article |
| id | doaj-art-b43fddba6ace4a2ea1cbca6ea3de88bf |
| institution | Kabale University |
| issn | 1006-2467 |
| language | zho |
| publishDate | 2025-01-01 |
| publisher | Editorial Office of Journal of Shanghai Jiao Tong University |
| record_format | Article |
| series | Shanghai Jiaotong Daxue xuebao |
| spelling | doaj-art-b43fddba6ace4a2ea1cbca6ea3de88bf2025-02-06T09:45:28ZzhoEditorial Office of Journal of Shanghai Jiao Tong UniversityShanghai Jiaotong Daxue xuebao1006-24672025-01-01591707810.16183/j.cnki.jsjtu.2023.188Optimization Method of Underwater Flapping Foil Propulsion Performance Based on Gaussian Process Regression and Deep Reinforcement LearningYANG Yinghe, WEI Handi, FAN Dixia, LI Ang01. State Key Laboratory of Ocean Engineering, Shanghai Jiao Tong University, Shanghai 200240, China;2. SJTU Yazhou Bay Institute of Deepsea Sci-Tech, Sanya 572024, Hainan, China;3. School of Engineering, Westlake University, Hangzhou 310024, ChinaIn order to overcome the complexity and variability of underwater working environments, as well as the difficulty of controlling the flapping motion due to the significant nonlinear characteristics and numerous variables involved, a direct exploration approach is proposed to search for the optimal flapping foil propulsion parameters in the environment. The Latin hypercube sampling technique is utilized to obtain the samples of multi-dimensional flapping parameters in actual water pool data, and a Gaussian process regression (GPR) machine learning model is established based on these samples to generalize the working environment. Under different propulsion performance requirements, the TD3 algorithm in deep reinforcement learning (DRL) is trained for maximizing rewards and obtaining the optimal combination of multiple parameter actions in continuous intervals. The experimental results demonstrate that the GPR-TD3 method is capable of learning the globally optimal solution for flapping propulsion in the experimental environment, including maximum speed and maximum efficiency. Furthermore, the accuracy of this optimal solution can be intuitively verified through a two-dimensional contour plot in the GPR. Meanwhile, with 290 sets of real samples provided for any given propulsion speed requirement, the agent can recommend a set of action combinations with an error range of 0.23% to 6.68%, which can provide reference for practical applications.https://xuebao.sjtu.edu.cn/article/2025/1006-2467/1006-2467-59-1-70.shtmlunderwater flapping foilgaussian process regression (gpr)deep reinforcement learning (drl)propulsion performance optimization |
| spellingShingle | YANG Yinghe, WEI Handi, FAN Dixia, LI Ang Optimization Method of Underwater Flapping Foil Propulsion Performance Based on Gaussian Process Regression and Deep Reinforcement Learning Shanghai Jiaotong Daxue xuebao underwater flapping foil gaussian process regression (gpr) deep reinforcement learning (drl) propulsion performance optimization |
| title | Optimization Method of Underwater Flapping Foil Propulsion Performance Based on Gaussian Process Regression and Deep Reinforcement Learning |
| title_full | Optimization Method of Underwater Flapping Foil Propulsion Performance Based on Gaussian Process Regression and Deep Reinforcement Learning |
| title_fullStr | Optimization Method of Underwater Flapping Foil Propulsion Performance Based on Gaussian Process Regression and Deep Reinforcement Learning |
| title_full_unstemmed | Optimization Method of Underwater Flapping Foil Propulsion Performance Based on Gaussian Process Regression and Deep Reinforcement Learning |
| title_short | Optimization Method of Underwater Flapping Foil Propulsion Performance Based on Gaussian Process Regression and Deep Reinforcement Learning |
| title_sort | optimization method of underwater flapping foil propulsion performance based on gaussian process regression and deep reinforcement learning |
| topic | underwater flapping foil gaussian process regression (gpr) deep reinforcement learning (drl) propulsion performance optimization |
| url | https://xuebao.sjtu.edu.cn/article/2025/1006-2467/1006-2467-59-1-70.shtml |
| work_keys_str_mv | AT yangyingheweihandifandixialiang optimizationmethodofunderwaterflappingfoilpropulsionperformancebasedongaussianprocessregressionanddeepreinforcementlearning |