Data-driven model identification and predictive control for path-following of underactuated ships with unknown dynamics
With the trends towards autonomous shipping, advanced ship motion control methods have received increased attention in recent years. The validity of ship models is crucial in designing motion controllers and directly affects their performances. However, accurate models that could reflect true ship d...
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| Format: | Article |
| Language: | English |
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Elsevier
2022-01-01
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| Series: | International Journal of Naval Architecture and Ocean Engineering |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2092678222000115 |
| _version_ | 1811182916531650560 |
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| author | Le Wang Shijie Li Jialun Liu Qing Wu |
| author_facet | Le Wang Shijie Li Jialun Liu Qing Wu |
| author_sort | Le Wang |
| collection | DOAJ |
| description | With the trends towards autonomous shipping, advanced ship motion control methods have received increased attention in recent years. The validity of ship models is crucial in designing motion controllers and directly affects their performances. However, accurate models that could reflect true ship dynamics are highly nonlinear, complex and complicated to identify, especially in situations when the experimental conditions are limited. This paper proposes a data-driven predictive control method for path-following of under-actuated cargo ships with unknown dynamics, which makes use of data gathered during operation to improve the model and the path-following performance. Based on the ship navigation data set, the relations between the heading angle and the rudder angle of the ship are fitted with seven typical regression algorithms, which acts as the prediction model in the path-following controller. Simulation study is carried out to choose the most suitable regression algorithm, among which elastic net regression is selected. The Antenna Mutation Beetle Swarm Predictive (AMBS-P) algorithm is introduced to find the optimal weights in the model identification process. A Line-of-Sight (LOS) algorithm is used as the guidance law to transform reference way-points into reference heading angles, and the path-following controller is designed also based on and the AMBS-P algorithm. Simulation results show that the proposed data-driven control method performs well in the path-following task without having prior knowledge regarding the hydrodynamic coefficients and ship parameters. |
| first_indexed | 2024-04-11T09:38:31Z |
| format | Article |
| id | doaj.art-282eb1e41f3640049a5488c8352de3a9 |
| institution | Directory Open Access Journal |
| issn | 2092-6782 |
| language | English |
| last_indexed | 2024-04-11T09:38:31Z |
| publishDate | 2022-01-01 |
| publisher | Elsevier |
| record_format | Article |
| series | International Journal of Naval Architecture and Ocean Engineering |
| spelling | doaj.art-282eb1e41f3640049a5488c8352de3a92022-12-22T04:31:20ZengElsevierInternational Journal of Naval Architecture and Ocean Engineering2092-67822022-01-0114100445Data-driven model identification and predictive control for path-following of underactuated ships with unknown dynamicsLe Wang0Shijie Li1Jialun Liu2Qing Wu3School of Transportation and Logistics Engineering, Wuhan University of Technology, Wuhan, PR ChinaSchool of Transportation and Logistics Engineering, Wuhan University of Technology, Wuhan, PR ChinaIntelligent Transportation Systems Research Center, Wuhan University of Technology, Wuhan, PR China; National Engineering Research Center for Water Transport Safety, Wuhan, PR China; Corresponding author.School of Transportation and Logistics Engineering, Wuhan University of Technology, Wuhan, PR ChinaWith the trends towards autonomous shipping, advanced ship motion control methods have received increased attention in recent years. The validity of ship models is crucial in designing motion controllers and directly affects their performances. However, accurate models that could reflect true ship dynamics are highly nonlinear, complex and complicated to identify, especially in situations when the experimental conditions are limited. This paper proposes a data-driven predictive control method for path-following of under-actuated cargo ships with unknown dynamics, which makes use of data gathered during operation to improve the model and the path-following performance. Based on the ship navigation data set, the relations between the heading angle and the rudder angle of the ship are fitted with seven typical regression algorithms, which acts as the prediction model in the path-following controller. Simulation study is carried out to choose the most suitable regression algorithm, among which elastic net regression is selected. The Antenna Mutation Beetle Swarm Predictive (AMBS-P) algorithm is introduced to find the optimal weights in the model identification process. A Line-of-Sight (LOS) algorithm is used as the guidance law to transform reference way-points into reference heading angles, and the path-following controller is designed also based on and the AMBS-P algorithm. Simulation results show that the proposed data-driven control method performs well in the path-following task without having prior knowledge regarding the hydrodynamic coefficients and ship parameters.http://www.sciencedirect.com/science/article/pii/S2092678222000115Model identificationAMBS-PElastic net regressionPath following |
| spellingShingle | Le Wang Shijie Li Jialun Liu Qing Wu Data-driven model identification and predictive control for path-following of underactuated ships with unknown dynamics International Journal of Naval Architecture and Ocean Engineering Model identification AMBS-P Elastic net regression Path following |
| title | Data-driven model identification and predictive control for path-following of underactuated ships with unknown dynamics |
| title_full | Data-driven model identification and predictive control for path-following of underactuated ships with unknown dynamics |
| title_fullStr | Data-driven model identification and predictive control for path-following of underactuated ships with unknown dynamics |
| title_full_unstemmed | Data-driven model identification and predictive control for path-following of underactuated ships with unknown dynamics |
| title_short | Data-driven model identification and predictive control for path-following of underactuated ships with unknown dynamics |
| title_sort | data driven model identification and predictive control for path following of underactuated ships with unknown dynamics |
| topic | Model identification AMBS-P Elastic net regression Path following |
| url | http://www.sciencedirect.com/science/article/pii/S2092678222000115 |
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