Balancing accuracy and convergence rate: a hybrid optimisation algorithm for parameter identification of unmanned marine vehicles
In this paper, a novel and well-balanced hybrid optimisation algorithm called NMA is proposed for parameter identification of a coupled three-degree-of-freedom unmanned marine vehicle in the presence of measurement noise. Firstly, a multi-step iterative prediction model is designed as identification...
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| Language: | English |
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Taylor & Francis Group
2025-12-01
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| Series: | Engineering Applications of Computational Fluid Mechanics |
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| Online Access: | https://www.tandfonline.com/doi/10.1080/19942060.2025.2503779 |
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| author | Zheng Li Caoyang Yu Yiming Zhong Yuanju Cao Xianbo Xiang Lian Lian |
| author_facet | Zheng Li Caoyang Yu Yiming Zhong Yuanju Cao Xianbo Xiang Lian Lian |
| author_sort | Zheng Li |
| collection | DOAJ |
| description | In this paper, a novel and well-balanced hybrid optimisation algorithm called NMA is proposed for parameter identification of a coupled three-degree-of-freedom unmanned marine vehicle in the presence of measurement noise. Firstly, a multi-step iterative prediction model is designed as identification structure, which uses the previous prediction as the current input instead of the measurement, thereby ensuring that the predictions are not affected by measurement noise. In this structure, the loss function incorporates residuals from all degrees of freedom, thus enabling a comprehensive optimisation of the parameters. Secondly, the proposed NMA algorithm integrates the Adam algorithm to guide the search of the Nelder-Mead (NM) simplex algorithm. This integration not only enhances the convergence rate but also improves the accuracy, ultimately achieving satisfactory optimisation performance. Thirdly, zero-order gradient estimators are introduced to replace the finite difference method in the original NMA algorithm, reducing the computational cost and resulting in a variant called ZO-NMA. Finally, the simulation results demonstrate that the proposed NMA algorithm significantly outperforms the individual algorithms. Specifically, it reduces the computation time by 27.09% compared to the existing Adam algorithm, while improving the accuracy by 13.2 percentage points compared to the standard NM simplex algorithm. Moreover, ZO-NMA further reduces computation time by 19.05% compared to the NMA algorithm. |
| format | Article |
| id | doaj-art-ea6ef086e57e404bafeb9541bdef2c3b |
| institution | OA Journals |
| issn | 1994-2060 1997-003X |
| language | English |
| publishDate | 2025-12-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Engineering Applications of Computational Fluid Mechanics |
| spelling | doaj-art-ea6ef086e57e404bafeb9541bdef2c3b2025-08-20T01:51:03ZengTaylor & Francis GroupEngineering Applications of Computational Fluid Mechanics1994-20601997-003X2025-12-0119110.1080/19942060.2025.2503779Balancing accuracy and convergence rate: a hybrid optimisation algorithm for parameter identification of unmanned marine vehiclesZheng Li0Caoyang Yu1Yiming Zhong2Yuanju Cao3Xianbo Xiang4Lian Lian5State Key Laboratory of Submarine Geoscience; Key Laboratory of Polar Ecosystem and Climate Change, Ministry of Education; Shanghai Key Laboratory of Polar Life and Environment Sciences; and School of Oceanography, Shanghai Jiao Tong University, Shanghai, People's Republic of ChinaState Key Laboratory of Submarine Geoscience; Key Laboratory of Polar Ecosystem and Climate Change, Ministry of Education; Shanghai Key Laboratory of Polar Life and Environment Sciences; and School of Oceanography, Shanghai Jiao Tong University, Shanghai, People's Republic of ChinaState Key Laboratory of Submarine Geoscience; Key Laboratory of Polar Ecosystem and Climate Change, Ministry of Education; Shanghai Key Laboratory of Polar Life and Environment Sciences; and School of Oceanography, Shanghai Jiao Tong University, Shanghai, People's Republic of ChinaState Key Laboratory of Submarine Geoscience; Key Laboratory of Polar Ecosystem and Climate Change, Ministry of Education; Shanghai Key Laboratory of Polar Life and Environment Sciences; and School of Oceanography, Shanghai Jiao Tong University, Shanghai, People's Republic of ChinaSchool of Naval Architecture and Ocean Engineering, Huazhong University of Science and Technology, Wuhan, People's Republic of ChinaState Key Laboratory of Submarine Geoscience; Key Laboratory of Polar Ecosystem and Climate Change, Ministry of Education; Shanghai Key Laboratory of Polar Life and Environment Sciences; and School of Oceanography, Shanghai Jiao Tong University, Shanghai, People's Republic of ChinaIn this paper, a novel and well-balanced hybrid optimisation algorithm called NMA is proposed for parameter identification of a coupled three-degree-of-freedom unmanned marine vehicle in the presence of measurement noise. Firstly, a multi-step iterative prediction model is designed as identification structure, which uses the previous prediction as the current input instead of the measurement, thereby ensuring that the predictions are not affected by measurement noise. In this structure, the loss function incorporates residuals from all degrees of freedom, thus enabling a comprehensive optimisation of the parameters. Secondly, the proposed NMA algorithm integrates the Adam algorithm to guide the search of the Nelder-Mead (NM) simplex algorithm. This integration not only enhances the convergence rate but also improves the accuracy, ultimately achieving satisfactory optimisation performance. Thirdly, zero-order gradient estimators are introduced to replace the finite difference method in the original NMA algorithm, reducing the computational cost and resulting in a variant called ZO-NMA. Finally, the simulation results demonstrate that the proposed NMA algorithm significantly outperforms the individual algorithms. Specifically, it reduces the computation time by 27.09% compared to the existing Adam algorithm, while improving the accuracy by 13.2 percentage points compared to the standard NM simplex algorithm. Moreover, ZO-NMA further reduces computation time by 19.05% compared to the NMA algorithm.https://www.tandfonline.com/doi/10.1080/19942060.2025.2503779Unmanned marine vehicleparameter identificationmanoeuvring predictionNelder–Mead simplex algorithmAdam algorithmzero-order optimisation |
| spellingShingle | Zheng Li Caoyang Yu Yiming Zhong Yuanju Cao Xianbo Xiang Lian Lian Balancing accuracy and convergence rate: a hybrid optimisation algorithm for parameter identification of unmanned marine vehicles Engineering Applications of Computational Fluid Mechanics Unmanned marine vehicle parameter identification manoeuvring prediction Nelder–Mead simplex algorithm Adam algorithm zero-order optimisation |
| title | Balancing accuracy and convergence rate: a hybrid optimisation algorithm for parameter identification of unmanned marine vehicles |
| title_full | Balancing accuracy and convergence rate: a hybrid optimisation algorithm for parameter identification of unmanned marine vehicles |
| title_fullStr | Balancing accuracy and convergence rate: a hybrid optimisation algorithm for parameter identification of unmanned marine vehicles |
| title_full_unstemmed | Balancing accuracy and convergence rate: a hybrid optimisation algorithm for parameter identification of unmanned marine vehicles |
| title_short | Balancing accuracy and convergence rate: a hybrid optimisation algorithm for parameter identification of unmanned marine vehicles |
| title_sort | balancing accuracy and convergence rate a hybrid optimisation algorithm for parameter identification of unmanned marine vehicles |
| topic | Unmanned marine vehicle parameter identification manoeuvring prediction Nelder–Mead simplex algorithm Adam algorithm zero-order optimisation |
| url | https://www.tandfonline.com/doi/10.1080/19942060.2025.2503779 |
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