Black-Box Modelling and Prediction of Deep-Sea Landing Vehicles Based on Optimised Support Vector Regression
Due to the nonlinearity of the deep-seafloor and complexity of the hydrodynamic force of novel structure platforms, realising an accurate motion mechanism modelling of a deep-sea landing vehicle (DSLV) is difficult. The support vector regression (SVR) model optimised through particle swarm optimisat...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-04-01
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| Series: | Journal of Marine Science and Engineering |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2077-1312/10/5/575 |
| _version_ | 1797498800872357888 |
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| author | Hongming Sun Wei Guo Yanjun Lan Zhenzhuo Wei Sen Gao Yu Sun Yifan Fu |
| author_facet | Hongming Sun Wei Guo Yanjun Lan Zhenzhuo Wei Sen Gao Yu Sun Yifan Fu |
| author_sort | Hongming Sun |
| collection | DOAJ |
| description | Due to the nonlinearity of the deep-seafloor and complexity of the hydrodynamic force of novel structure platforms, realising an accurate motion mechanism modelling of a deep-sea landing vehicle (DSLV) is difficult. The support vector regression (SVR) model optimised through particle swarm optimisation (PSO) was used to complete the black-box motion modelling and vehicle prediction. In this study, first, the prototype and system composition of the DSLV were proposed, and subsequently, the high-dimensional nonlinear mapping relationship between the motion state and the driving forces was constructed using the SVR of radial basis function. The high-precision model parameter combination was obtained using PSO, and, subsequently, the black-box modelling and prediction of the vehicle were realised. Finally, the effectiveness of the method was verified through multi-body dynamics simulation and scaled test prototype data. The experimental results confirmed that the proposed PSO–SVR model could establish an accurate motion model of the vehicle, and provided a high-precision motion state prediction. Furthermore, with less calculation, the proposed method can reliably apply the model prediction results to the intelligent behaviour control and planning of the vehicle, accelerate the development progress of the prototype, and minimise the economic cost of the research and development process. |
| first_indexed | 2024-03-10T03:38:35Z |
| format | Article |
| id | doaj.art-5762302edf8b43ca9e74e6784a72c660 |
| institution | Directory Open Access Journal |
| issn | 2077-1312 |
| language | English |
| last_indexed | 2024-03-10T03:38:35Z |
| publishDate | 2022-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Journal of Marine Science and Engineering |
| spelling | doaj.art-5762302edf8b43ca9e74e6784a72c6602023-11-23T11:38:41ZengMDPI AGJournal of Marine Science and Engineering2077-13122022-04-0110557510.3390/jmse10050575Black-Box Modelling and Prediction of Deep-Sea Landing Vehicles Based on Optimised Support Vector RegressionHongming Sun0Wei Guo1Yanjun Lan2Zhenzhuo Wei3Sen Gao4Yu Sun5Yifan Fu6Institute of Deep-Sea Science and Engineering, Chinese Academy of Sciences, Hainan 572000, ChinaInstitute of Deep-Sea Science and Engineering, Chinese Academy of Sciences, Hainan 572000, ChinaInstitute of Deep-Sea Science and Engineering, Chinese Academy of Sciences, Hainan 572000, ChinaInstitute of Deep-Sea Science and Engineering, Chinese Academy of Sciences, Hainan 572000, ChinaInstitute of Deep-Sea Science and Engineering, Chinese Academy of Sciences, Hainan 572000, ChinaInstitute of Deep-Sea Science and Engineering, Chinese Academy of Sciences, Hainan 572000, ChinaInstitute of Deep-Sea Science and Engineering, Chinese Academy of Sciences, Hainan 572000, ChinaDue to the nonlinearity of the deep-seafloor and complexity of the hydrodynamic force of novel structure platforms, realising an accurate motion mechanism modelling of a deep-sea landing vehicle (DSLV) is difficult. The support vector regression (SVR) model optimised through particle swarm optimisation (PSO) was used to complete the black-box motion modelling and vehicle prediction. In this study, first, the prototype and system composition of the DSLV were proposed, and subsequently, the high-dimensional nonlinear mapping relationship between the motion state and the driving forces was constructed using the SVR of radial basis function. The high-precision model parameter combination was obtained using PSO, and, subsequently, the black-box modelling and prediction of the vehicle were realised. Finally, the effectiveness of the method was verified through multi-body dynamics simulation and scaled test prototype data. The experimental results confirmed that the proposed PSO–SVR model could establish an accurate motion model of the vehicle, and provided a high-precision motion state prediction. Furthermore, with less calculation, the proposed method can reliably apply the model prediction results to the intelligent behaviour control and planning of the vehicle, accelerate the development progress of the prototype, and minimise the economic cost of the research and development process.https://www.mdpi.com/2077-1312/10/5/575deep-sea landing vehicleblack-box modellingsupport vector regressionparticle swarm optimisation |
| spellingShingle | Hongming Sun Wei Guo Yanjun Lan Zhenzhuo Wei Sen Gao Yu Sun Yifan Fu Black-Box Modelling and Prediction of Deep-Sea Landing Vehicles Based on Optimised Support Vector Regression Journal of Marine Science and Engineering deep-sea landing vehicle black-box modelling support vector regression particle swarm optimisation |
| title | Black-Box Modelling and Prediction of Deep-Sea Landing Vehicles Based on Optimised Support Vector Regression |
| title_full | Black-Box Modelling and Prediction of Deep-Sea Landing Vehicles Based on Optimised Support Vector Regression |
| title_fullStr | Black-Box Modelling and Prediction of Deep-Sea Landing Vehicles Based on Optimised Support Vector Regression |
| title_full_unstemmed | Black-Box Modelling and Prediction of Deep-Sea Landing Vehicles Based on Optimised Support Vector Regression |
| title_short | Black-Box Modelling and Prediction of Deep-Sea Landing Vehicles Based on Optimised Support Vector Regression |
| title_sort | black box modelling and prediction of deep sea landing vehicles based on optimised support vector regression |
| topic | deep-sea landing vehicle black-box modelling support vector regression particle swarm optimisation |
| url | https://www.mdpi.com/2077-1312/10/5/575 |
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