An adaptive NMPC for ROVs trajectory tracking with environmental disturbances and model uncertainties
A novel hybrid adaptive control method is presented for trajectory tracking of remotely operated underwater vehicles (ROVs) that addresses unknown disturbances and model uncertainties in this paper. Traditional nonlinear control methods struggle to handle external disturbances and uncertainty in sys...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Faculty of Mechanical Engineering and Naval Architecture
2025-01-01
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| Series: | Brodogradnja |
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| Online Access: | https://hrcak.srce.hr/file/471941 |
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| author | Yi Zhang Jianing Zhang Zhiyang Guo Lei Zhang Yuchen Shang |
| author_facet | Yi Zhang Jianing Zhang Zhiyang Guo Lei Zhang Yuchen Shang |
| author_sort | Yi Zhang |
| collection | DOAJ |
| description | A novel hybrid adaptive control method is presented for trajectory tracking of remotely operated underwater vehicles (ROVs) that addresses unknown disturbances and model uncertainties in this paper. Traditional nonlinear control methods struggle to handle external disturbances and uncertainty in system model. To address the trajectory tracking control needs of ROVs in complex underwater environments, a kinematic and dynamic model is first developed for a fully actuated ROV with six degrees of freedom (6-DOF). The trajectory tracking problem is formulated as an online, nonlinear receding horizon optimization process. Control increments are computed as inputs to this nonlinear optimization problem. An L1 adaptive control method (L1AC) is then developed, incorporating a state observer, adaptive control law, and time filter. The framework retains the rolling optimization process of nonlinear model predictive control (NMPC) while integrating the L1 adaptive component for instant compensation of unknown disturbances and model parameter mismatches. Numerical simulations were conducted to compare the trajectory tracking performance of the proposed hybrid adaptive method with the NMPC method under various disturbances, including ocean currents, waves, random forces, and model uncertainties. The results confirm that the proposed hybrid adaptive control scheme is more effective and robust than the standalone NMPC approach across various scenarios. |
| format | Article |
| id | doaj-art-76fa9e0fef2b44b2be71231db2a52c22 |
| institution | Kabale University |
| issn | 0007-215X 1845-5859 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Faculty of Mechanical Engineering and Naval Architecture |
| record_format | Article |
| series | Brodogradnja |
| spelling | doaj-art-76fa9e0fef2b44b2be71231db2a52c222025-01-09T11:41:04ZengFaculty of Mechanical Engineering and Naval ArchitectureBrodogradnja0007-215X1845-58592025-01-0176112510.21278/brod76106An adaptive NMPC for ROVs trajectory tracking with environmental disturbances and model uncertaintiesYi Zhang0Jianing Zhang1Zhiyang Guo2Lei Zhang3Yuchen Shang4School of Naval Architecture and Ocean Engineering, Dalian Maritime University, Dalian 116026, ChinaSchool of Naval Architecture and Ocean Engineering, Dalian Maritime University, Dalian 116026, ChinaSchool of Naval Architecture and Ocean Engineering, Dalian Maritime University, Dalian 116026, ChinaSchool of Naval Architecture and Ocean Engineering, Dalian Maritime University, Dalian 116026, ChinaDepartment of Ocean Engineering, Texas A&M University College, Station 77843, USAA novel hybrid adaptive control method is presented for trajectory tracking of remotely operated underwater vehicles (ROVs) that addresses unknown disturbances and model uncertainties in this paper. Traditional nonlinear control methods struggle to handle external disturbances and uncertainty in system model. To address the trajectory tracking control needs of ROVs in complex underwater environments, a kinematic and dynamic model is first developed for a fully actuated ROV with six degrees of freedom (6-DOF). The trajectory tracking problem is formulated as an online, nonlinear receding horizon optimization process. Control increments are computed as inputs to this nonlinear optimization problem. An L1 adaptive control method (L1AC) is then developed, incorporating a state observer, adaptive control law, and time filter. The framework retains the rolling optimization process of nonlinear model predictive control (NMPC) while integrating the L1 adaptive component for instant compensation of unknown disturbances and model parameter mismatches. Numerical simulations were conducted to compare the trajectory tracking performance of the proposed hybrid adaptive method with the NMPC method under various disturbances, including ocean currents, waves, random forces, and model uncertainties. The results confirm that the proposed hybrid adaptive control scheme is more effective and robust than the standalone NMPC approach across various scenarios.https://hrcak.srce.hr/file/471941remotely operated underwater vehicles (rovs)trajectory trackingan adaptive nmpcunknown disturbancesmodel uncertainties |
| spellingShingle | Yi Zhang Jianing Zhang Zhiyang Guo Lei Zhang Yuchen Shang An adaptive NMPC for ROVs trajectory tracking with environmental disturbances and model uncertainties Brodogradnja remotely operated underwater vehicles (rovs) trajectory tracking an adaptive nmpc unknown disturbances model uncertainties |
| title | An adaptive NMPC for ROVs trajectory tracking with environmental disturbances and model uncertainties |
| title_full | An adaptive NMPC for ROVs trajectory tracking with environmental disturbances and model uncertainties |
| title_fullStr | An adaptive NMPC for ROVs trajectory tracking with environmental disturbances and model uncertainties |
| title_full_unstemmed | An adaptive NMPC for ROVs trajectory tracking with environmental disturbances and model uncertainties |
| title_short | An adaptive NMPC for ROVs trajectory tracking with environmental disturbances and model uncertainties |
| title_sort | adaptive nmpc for rovs trajectory tracking with environmental disturbances and model uncertainties |
| topic | remotely operated underwater vehicles (rovs) trajectory tracking an adaptive nmpc unknown disturbances model uncertainties |
| url | https://hrcak.srce.hr/file/471941 |
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