Spatial Trajectory Tracking of Underactuated Autonomous Underwater Vehicles by Model–Data-Driven Learning Adaptive Robust Control
This paper aims to solve the spatial trajectory tracking control problem of underactuated autonomous underwater vehicles (AUVs) in the presence of system parameter uncertainties and complex external disturbances. To accomplish this goal, a model–data-driven learning adaptive robust control (LARC) st...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-06-01
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| Series: | Journal of Marine Science and Engineering |
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| Online Access: | https://www.mdpi.com/2077-1312/13/6/1151 |
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| author | Linyuan Guo Ran Zhou Qingchang Guo Liran Ma Chuxiong Hu Jianbin Luo |
| author_facet | Linyuan Guo Ran Zhou Qingchang Guo Liran Ma Chuxiong Hu Jianbin Luo |
| author_sort | Linyuan Guo |
| collection | DOAJ |
| description | This paper aims to solve the spatial trajectory tracking control problem of underactuated autonomous underwater vehicles (AUVs) in the presence of system parameter uncertainties and complex external disturbances. To accomplish this goal, a model–data-driven learning adaptive robust control (LARC) strategy is introduced for AUVs. Firstly, a serial iterative learning control (ILC) approach is introduced as feedforward compensation, and then the corresponding trajectory tracking error dynamics model, the Feedforward Compensation–Line of Sight (FFC-LOS) guidance law, and the feedforward compensation-based kinematics controller are designed. Secondly, the dynamics controller is designed for AUVs, which consists of a linear feedback term, a nonlinear robust feedback term, an adjustable model compensation term, and a fast dynamic compensation term. In this control framework, the robust control and fast dynamic compensation parts are utilized to deal with nonlinear uncertainties and disturbances, the projection-type adaptive control part solves the influence caused by the uncertainty of system parameters, and the serial ILC part that is a data-driven learning method can further improve the trajectory tracking accuracy for repetitive tasks. Finally, comparative simulations under different scenarios and different types of disturbances are performed to verify the effectiveness of the proposed control strategy for AUVs. |
| format | Article |
| id | doaj-art-0348caa74f614d6396db57d447171aef |
| institution | Kabale University |
| issn | 2077-1312 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Journal of Marine Science and Engineering |
| spelling | doaj-art-0348caa74f614d6396db57d447171aef2025-08-20T03:27:33ZengMDPI AGJournal of Marine Science and Engineering2077-13122025-06-01136115110.3390/jmse13061151Spatial Trajectory Tracking of Underactuated Autonomous Underwater Vehicles by Model–Data-Driven Learning Adaptive Robust ControlLinyuan Guo0Ran Zhou1Qingchang Guo2Liran Ma3Chuxiong Hu4Jianbin Luo5Department of Mechanical Engineering, Tsinghua University, Beijing 100084, ChinaDepartment of Mechanical Engineering, Tsinghua University, Beijing 100084, ChinaState Key Laboratory of Tribology in Advanced Equipment, Tsinghua University, Beijing 100084, ChinaDepartment of Mechanical Engineering, Tsinghua University, Beijing 100084, ChinaDepartment of Mechanical Engineering, Tsinghua University, Beijing 100084, ChinaDepartment of Mechanical Engineering, Tsinghua University, Beijing 100084, ChinaThis paper aims to solve the spatial trajectory tracking control problem of underactuated autonomous underwater vehicles (AUVs) in the presence of system parameter uncertainties and complex external disturbances. To accomplish this goal, a model–data-driven learning adaptive robust control (LARC) strategy is introduced for AUVs. Firstly, a serial iterative learning control (ILC) approach is introduced as feedforward compensation, and then the corresponding trajectory tracking error dynamics model, the Feedforward Compensation–Line of Sight (FFC-LOS) guidance law, and the feedforward compensation-based kinematics controller are designed. Secondly, the dynamics controller is designed for AUVs, which consists of a linear feedback term, a nonlinear robust feedback term, an adjustable model compensation term, and a fast dynamic compensation term. In this control framework, the robust control and fast dynamic compensation parts are utilized to deal with nonlinear uncertainties and disturbances, the projection-type adaptive control part solves the influence caused by the uncertainty of system parameters, and the serial ILC part that is a data-driven learning method can further improve the trajectory tracking accuracy for repetitive tasks. Finally, comparative simulations under different scenarios and different types of disturbances are performed to verify the effectiveness of the proposed control strategy for AUVs.https://www.mdpi.com/2077-1312/13/6/1151underactuated AUVsspatial trajectory trackingmotion controladaptive controldata-driven control |
| spellingShingle | Linyuan Guo Ran Zhou Qingchang Guo Liran Ma Chuxiong Hu Jianbin Luo Spatial Trajectory Tracking of Underactuated Autonomous Underwater Vehicles by Model–Data-Driven Learning Adaptive Robust Control Journal of Marine Science and Engineering underactuated AUVs spatial trajectory tracking motion control adaptive control data-driven control |
| title | Spatial Trajectory Tracking of Underactuated Autonomous Underwater Vehicles by Model–Data-Driven Learning Adaptive Robust Control |
| title_full | Spatial Trajectory Tracking of Underactuated Autonomous Underwater Vehicles by Model–Data-Driven Learning Adaptive Robust Control |
| title_fullStr | Spatial Trajectory Tracking of Underactuated Autonomous Underwater Vehicles by Model–Data-Driven Learning Adaptive Robust Control |
| title_full_unstemmed | Spatial Trajectory Tracking of Underactuated Autonomous Underwater Vehicles by Model–Data-Driven Learning Adaptive Robust Control |
| title_short | Spatial Trajectory Tracking of Underactuated Autonomous Underwater Vehicles by Model–Data-Driven Learning Adaptive Robust Control |
| title_sort | spatial trajectory tracking of underactuated autonomous underwater vehicles by model data driven learning adaptive robust control |
| topic | underactuated AUVs spatial trajectory tracking motion control adaptive control data-driven control |
| url | https://www.mdpi.com/2077-1312/13/6/1151 |
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