Model Predictive Control Based on State Space and Risk Augmentation for Unmanned Surface Vessel Trajectory Tracking
The underactuated unmanned surface vessel (USV) has been identified as a promising solution for future maritime transport. However, the challenges of precise trajectory tracking and obstacle avoidance remain unresolved for USVs. To this end, this paper models the problem of path tracking through the...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-11-01
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| Series: | Journal of Marine Science and Engineering |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2077-1312/11/12/2283 |
| _version_ | 1797380471916593152 |
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| author | Wei Li Jun Zhang Fang Wang Hanyun Zhou |
| author_facet | Wei Li Jun Zhang Fang Wang Hanyun Zhou |
| author_sort | Wei Li |
| collection | DOAJ |
| description | The underactuated unmanned surface vessel (USV) has been identified as a promising solution for future maritime transport. However, the challenges of precise trajectory tracking and obstacle avoidance remain unresolved for USVs. To this end, this paper models the problem of path tracking through the first-order Nomoto model in the Serret–Frenet coordinate system. A novel risk model has been developed to depict the association between USVs and obstacles based on SFC. Combined with an artificial potential field that accounts for environmental obstacles, model predictive control (MPC) based on state space is employed to achieve the optimal control sequence. The stability of the designed controller is demonstrated by means of the Lyapunov method and zero-pole analysis. Through simulation, it has been demonstrated that the controller is asymptotically stable concerning track error deviation, heading angle deviation, and heading angle speed, and its good stability and robustness in the presence of multiple risks are verified. |
| first_indexed | 2024-03-08T20:37:45Z |
| format | Article |
| id | doaj.art-5a87b927234b4986addfe50002170d0a |
| institution | Directory Open Access Journal |
| issn | 2077-1312 |
| language | English |
| last_indexed | 2024-03-08T20:37:45Z |
| publishDate | 2023-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Journal of Marine Science and Engineering |
| spelling | doaj.art-5a87b927234b4986addfe50002170d0a2023-12-22T14:18:48ZengMDPI AGJournal of Marine Science and Engineering2077-13122023-11-011112228310.3390/jmse11122283Model Predictive Control Based on State Space and Risk Augmentation for Unmanned Surface Vessel Trajectory TrackingWei Li0Jun Zhang1Fang Wang2Hanyun Zhou3College of Information and Electrical Engineering, Hangzhou City University, Hangzhou 310015, ChinaSchool of Electrical Information Engineering, Jiangsu University, Zhenjiang 212013, ChinaCollege of Information and Electrical Engineering, Hangzhou City University, Hangzhou 310015, ChinaCollege of Information Engineering, Zhejiang University of Technology, Hangzhou 310023, ChinaThe underactuated unmanned surface vessel (USV) has been identified as a promising solution for future maritime transport. However, the challenges of precise trajectory tracking and obstacle avoidance remain unresolved for USVs. To this end, this paper models the problem of path tracking through the first-order Nomoto model in the Serret–Frenet coordinate system. A novel risk model has been developed to depict the association between USVs and obstacles based on SFC. Combined with an artificial potential field that accounts for environmental obstacles, model predictive control (MPC) based on state space is employed to achieve the optimal control sequence. The stability of the designed controller is demonstrated by means of the Lyapunov method and zero-pole analysis. Through simulation, it has been demonstrated that the controller is asymptotically stable concerning track error deviation, heading angle deviation, and heading angle speed, and its good stability and robustness in the presence of multiple risks are verified.https://www.mdpi.com/2077-1312/11/12/2283USVsmodel predictive controlobstacle avoidancetrajectory tackingrisk augmentation |
| spellingShingle | Wei Li Jun Zhang Fang Wang Hanyun Zhou Model Predictive Control Based on State Space and Risk Augmentation for Unmanned Surface Vessel Trajectory Tracking Journal of Marine Science and Engineering USVs model predictive control obstacle avoidance trajectory tacking risk augmentation |
| title | Model Predictive Control Based on State Space and Risk Augmentation for Unmanned Surface Vessel Trajectory Tracking |
| title_full | Model Predictive Control Based on State Space and Risk Augmentation for Unmanned Surface Vessel Trajectory Tracking |
| title_fullStr | Model Predictive Control Based on State Space and Risk Augmentation for Unmanned Surface Vessel Trajectory Tracking |
| title_full_unstemmed | Model Predictive Control Based on State Space and Risk Augmentation for Unmanned Surface Vessel Trajectory Tracking |
| title_short | Model Predictive Control Based on State Space and Risk Augmentation for Unmanned Surface Vessel Trajectory Tracking |
| title_sort | model predictive control based on state space and risk augmentation for unmanned surface vessel trajectory tracking |
| topic | USVs model predictive control obstacle avoidance trajectory tacking risk augmentation |
| url | https://www.mdpi.com/2077-1312/11/12/2283 |
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