An Integrated Method for Ship Heading Control Using Motion Model Prediction and Fractional Order Proportion Integration Differentiation Controller
Due to the influence of the natural environment, it is very challenging to control the movement of ships to navigate safely and avoid potential risks induced by external environmental factors, especially for the development of autonomous ships in inland or restricted waterways. In this research, we...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-12-01
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| Series: | Journal of Marine Science and Engineering |
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| Online Access: | https://www.mdpi.com/2077-1312/11/12/2294 |
| _version_ | 1797380450186952704 |
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| author | Xin Shi Pengfei Chen Linying Chen |
| author_facet | Xin Shi Pengfei Chen Linying Chen |
| author_sort | Xin Shi |
| collection | DOAJ |
| description | Due to the influence of the natural environment, it is very challenging to control the movement of ships to navigate safely and avoid potential risks induced by external environmental factors, especially for the development of autonomous ships in inland or restricted waterways. In this research, we propose an integrated approach for ship heading control that improves the timeliness and robustness of navigation. Recursive least squares and backward propagation neural networks are utilized to identify the ship motion model parameters under the influence of external factors and predict their development in real time. A particle swarm optimization-integrated Fractional Order Proportion Integration Differentiation (FOPID) controller is then designed based on the dynamically identified motion model to achieve accurate heading control for ships navigating in restricted waterways. A case study was conducted based on the Korea Venture Large Crude Carrier 2 (KVLCC2) model to verify the effectiveness, and a comparison between the conventional FOPID controller and the improved FOPID controller was also conducted. The results indicate that the proposed identification–prediction–optimization FOPID controller has faster speed on stabilization and has higher robustness against external influences, which could provide added value for the development of a motion controller for the autonomous ship for inland and restricted waterway navigation. |
| first_indexed | 2024-03-08T20:37:25Z |
| format | Article |
| id | doaj.art-0390a03e0e2f49d4b3bc8dfac062d77e |
| institution | Directory Open Access Journal |
| issn | 2077-1312 |
| language | English |
| last_indexed | 2024-03-08T20:37:25Z |
| publishDate | 2023-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Journal of Marine Science and Engineering |
| spelling | doaj.art-0390a03e0e2f49d4b3bc8dfac062d77e2023-12-22T14:18:50ZengMDPI AGJournal of Marine Science and Engineering2077-13122023-12-011112229410.3390/jmse11122294An Integrated Method for Ship Heading Control Using Motion Model Prediction and Fractional Order Proportion Integration Differentiation ControllerXin Shi0Pengfei Chen1Linying Chen2School of Navigation, Wuhan University of Technology, Wuhan 430070, ChinaSchool of Navigation, Wuhan University of Technology, Wuhan 430070, ChinaSchool of Navigation, Wuhan University of Technology, Wuhan 430070, ChinaDue to the influence of the natural environment, it is very challenging to control the movement of ships to navigate safely and avoid potential risks induced by external environmental factors, especially for the development of autonomous ships in inland or restricted waterways. In this research, we propose an integrated approach for ship heading control that improves the timeliness and robustness of navigation. Recursive least squares and backward propagation neural networks are utilized to identify the ship motion model parameters under the influence of external factors and predict their development in real time. A particle swarm optimization-integrated Fractional Order Proportion Integration Differentiation (FOPID) controller is then designed based on the dynamically identified motion model to achieve accurate heading control for ships navigating in restricted waterways. A case study was conducted based on the Korea Venture Large Crude Carrier 2 (KVLCC2) model to verify the effectiveness, and a comparison between the conventional FOPID controller and the improved FOPID controller was also conducted. The results indicate that the proposed identification–prediction–optimization FOPID controller has faster speed on stabilization and has higher robustness against external influences, which could provide added value for the development of a motion controller for the autonomous ship for inland and restricted waterway navigation.https://www.mdpi.com/2077-1312/11/12/2294ship motion mathematical modelonline identificationcourse controlneural networkleast squares method |
| spellingShingle | Xin Shi Pengfei Chen Linying Chen An Integrated Method for Ship Heading Control Using Motion Model Prediction and Fractional Order Proportion Integration Differentiation Controller Journal of Marine Science and Engineering ship motion mathematical model online identification course control neural network least squares method |
| title | An Integrated Method for Ship Heading Control Using Motion Model Prediction and Fractional Order Proportion Integration Differentiation Controller |
| title_full | An Integrated Method for Ship Heading Control Using Motion Model Prediction and Fractional Order Proportion Integration Differentiation Controller |
| title_fullStr | An Integrated Method for Ship Heading Control Using Motion Model Prediction and Fractional Order Proportion Integration Differentiation Controller |
| title_full_unstemmed | An Integrated Method for Ship Heading Control Using Motion Model Prediction and Fractional Order Proportion Integration Differentiation Controller |
| title_short | An Integrated Method for Ship Heading Control Using Motion Model Prediction and Fractional Order Proportion Integration Differentiation Controller |
| title_sort | integrated method for ship heading control using motion model prediction and fractional order proportion integration differentiation controller |
| topic | ship motion mathematical model online identification course control neural network least squares method |
| url | https://www.mdpi.com/2077-1312/11/12/2294 |
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