Research on the Control Problem of Autonomous Underwater Vehicles Based on Strongly Coupled Radial Basis Function Conditions
This paper addresses tracking control problems for autonomous underwater vehicle (AUV) systems with coupled nonlinear functions. For the first time, the radial basis function (RBF) is applied to the model reference adaptive control system, and the vehicle horizontal plane model is proposed. When the...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-06-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/13/13/7732 |
| _version_ | 1797592154224197632 |
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| author | Qinghe Zhang Longchuan Guo Md Abrar Hasan Sohan Xiaoqing Tian |
| author_facet | Qinghe Zhang Longchuan Guo Md Abrar Hasan Sohan Xiaoqing Tian |
| author_sort | Qinghe Zhang |
| collection | DOAJ |
| description | This paper addresses tracking control problems for autonomous underwater vehicle (AUV) systems with coupled nonlinear functions. For the first time, the radial basis function (RBF) is applied to the model reference adaptive control system, and the vehicle horizontal plane model is proposed. When the AUV movement is affected by the driving force, ocean resistance, and the force generated by the water current, the expected output of the AUV’s system is difficult to meet the expectations, making the AUV trajectory tracking problems challenging. There are two main options for finding suitable controllers for AUVs. The first is making the AUV model achieve better stability using a more complex controller. The second is the simpler controller structure, which can ensure faster system feedback. The RBF and model reference adaptive control (MEAC) system are combined to increase the number of hidden layers, increasing the AUV tracking stability. Because the embedded computing module of an AUV is a bit limited, 31 hidden layers are chosen to simplify the controller structures. A couple of Lyapunov functions are designed for the expected surge and sway velocities, and the vehicle tracking error gradually converges to (0,0). The controller design results are imported into the AUV actuator model by software, and after 0.64 s, the AUV tracking error is less than 1%. At last, the vehicle tracking experiments were carried out, showing that after 0.5 s, the AUV tracking error was less than 1%. |
| first_indexed | 2024-03-11T01:47:27Z |
| format | Article |
| id | doaj.art-caf7593ad5f04d9b941801e986fb8ec6 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-11T01:47:27Z |
| publishDate | 2023-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-caf7593ad5f04d9b941801e986fb8ec62023-11-18T16:10:38ZengMDPI AGApplied Sciences2076-34172023-06-011313773210.3390/app13137732Research on the Control Problem of Autonomous Underwater Vehicles Based on Strongly Coupled Radial Basis Function ConditionsQinghe Zhang0Longchuan Guo1Md Abrar Hasan Sohan2Xiaoqing Tian3School of Mechanical Engineering, Hangzhou Dianzi University, Hangzhou 310018, ChinaSchool of Mechanical Engineering, Hangzhou Dianzi University, Hangzhou 310018, ChinaSchool of International Education, Hangzhou Dianzi University, Hangzhou 310018, ChinaSchool of Mechanical Engineering, Hangzhou Dianzi University, Hangzhou 310018, ChinaThis paper addresses tracking control problems for autonomous underwater vehicle (AUV) systems with coupled nonlinear functions. For the first time, the radial basis function (RBF) is applied to the model reference adaptive control system, and the vehicle horizontal plane model is proposed. When the AUV movement is affected by the driving force, ocean resistance, and the force generated by the water current, the expected output of the AUV’s system is difficult to meet the expectations, making the AUV trajectory tracking problems challenging. There are two main options for finding suitable controllers for AUVs. The first is making the AUV model achieve better stability using a more complex controller. The second is the simpler controller structure, which can ensure faster system feedback. The RBF and model reference adaptive control (MEAC) system are combined to increase the number of hidden layers, increasing the AUV tracking stability. Because the embedded computing module of an AUV is a bit limited, 31 hidden layers are chosen to simplify the controller structures. A couple of Lyapunov functions are designed for the expected surge and sway velocities, and the vehicle tracking error gradually converges to (0,0). The controller design results are imported into the AUV actuator model by software, and after 0.64 s, the AUV tracking error is less than 1%. At last, the vehicle tracking experiments were carried out, showing that after 0.5 s, the AUV tracking error was less than 1%.https://www.mdpi.com/2076-3417/13/13/7732tracking systemuncertain nonlinear systemsAUVRBFMRAC |
| spellingShingle | Qinghe Zhang Longchuan Guo Md Abrar Hasan Sohan Xiaoqing Tian Research on the Control Problem of Autonomous Underwater Vehicles Based on Strongly Coupled Radial Basis Function Conditions Applied Sciences tracking system uncertain nonlinear systems AUV RBF MRAC |
| title | Research on the Control Problem of Autonomous Underwater Vehicles Based on Strongly Coupled Radial Basis Function Conditions |
| title_full | Research on the Control Problem of Autonomous Underwater Vehicles Based on Strongly Coupled Radial Basis Function Conditions |
| title_fullStr | Research on the Control Problem of Autonomous Underwater Vehicles Based on Strongly Coupled Radial Basis Function Conditions |
| title_full_unstemmed | Research on the Control Problem of Autonomous Underwater Vehicles Based on Strongly Coupled Radial Basis Function Conditions |
| title_short | Research on the Control Problem of Autonomous Underwater Vehicles Based on Strongly Coupled Radial Basis Function Conditions |
| title_sort | research on the control problem of autonomous underwater vehicles based on strongly coupled radial basis function conditions |
| topic | tracking system uncertain nonlinear systems AUV RBF MRAC |
| url | https://www.mdpi.com/2076-3417/13/13/7732 |
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