Event-Triggered Finite-Time Tracking Control of Unmanned Surface Vessel Using Neural Network Prescribed Performance
The current paper verifies the event-triggered finite-time tracking control of a fully actuated unmanned surface vessel under unmodeled dynamics and external disturbances. Radial basis function neural networks (RBFNNs), nonlinear disturbance observers (NDO), and the event-triggered mechanism (ETM) a...
| Main Authors: | , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
IEEE
2024-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10374356/ |
| _version_ | 1797330331729133568 |
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| author | Yuan Liu Li Zhao Wenfang Sun Qing Wang Guoxing Li Haiyang Guo Xinxiang Zhang Jiaming Zhang Zhiqing Bai |
| author_facet | Yuan Liu Li Zhao Wenfang Sun Qing Wang Guoxing Li Haiyang Guo Xinxiang Zhang Jiaming Zhang Zhiqing Bai |
| author_sort | Yuan Liu |
| collection | DOAJ |
| description | The current paper verifies the event-triggered finite-time tracking control of a fully actuated unmanned surface vessel under unmodeled dynamics and external disturbances. Radial basis function neural networks (RBFNNs), nonlinear disturbance observers (NDO), and the event-triggered mechanism (ETM) are utilized to design a new type of finite time tracking controller (FFTC). The proposed controller utilizes the dynamic surface control (DSC) approach to resolve the “differential explosion” issue of virtual control laws. Prescribed performance functions (PPFs) and the finite-time control (FFC) technique are utilized to specify the efficiency of tracking errors. The designed control laws make the control system of USV semi-globally practically finite-time stable (SGPFS) and make the tracking errors tend to a narrow residual set involving the specified bound in a finite time. In the end, the simulations reflect the presented FFTC’s efficiency. |
| first_indexed | 2024-03-08T07:18:21Z |
| format | Article |
| id | doaj.art-4b1b244d0b8340ac8b4b7ddfeab40287 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-03-08T07:18:21Z |
| publishDate | 2024-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-4b1b244d0b8340ac8b4b7ddfeab402872024-02-03T00:02:24ZengIEEEIEEE Access2169-35362024-01-0112114811149110.1109/ACCESS.2023.334756610374356Event-Triggered Finite-Time Tracking Control of Unmanned Surface Vessel Using Neural Network Prescribed PerformanceYuan Liu0https://orcid.org/0000-0003-4095-3781Li Zhao1Wenfang Sun2Qing Wang3Guoxing Li4Haiyang Guo5Xinxiang Zhang6Jiaming Zhang7Zhiqing Bai8School of Electronic Information and Intelligent Manufacturing, Anhui Xinhua University, Anhui, ChinaSchool of Mechatronic Engineering and Automation, Shanghai University, Shanghai, ChinaSchool of Electronic Information and Intelligent Manufacturing, Anhui Xinhua University, Anhui, ChinaSchool of Electronic Information and Intelligent Manufacturing, Anhui Xinhua University, Anhui, ChinaSchool of Electronic Information and Intelligent Manufacturing, Anhui Xinhua University, Anhui, ChinaSchool of Electronic Information and Intelligent Manufacturing, Anhui Xinhua University, Anhui, ChinaSchool of Electronic Information and Intelligent Manufacturing, Anhui Xinhua University, Anhui, ChinaSchool of Mechatronic Engineering and Automation, Shanghai University, Shanghai, ChinaSchool of Electronic Information and Intelligent Manufacturing, Anhui Xinhua University, Anhui, ChinaThe current paper verifies the event-triggered finite-time tracking control of a fully actuated unmanned surface vessel under unmodeled dynamics and external disturbances. Radial basis function neural networks (RBFNNs), nonlinear disturbance observers (NDO), and the event-triggered mechanism (ETM) are utilized to design a new type of finite time tracking controller (FFTC). The proposed controller utilizes the dynamic surface control (DSC) approach to resolve the “differential explosion” issue of virtual control laws. Prescribed performance functions (PPFs) and the finite-time control (FFC) technique are utilized to specify the efficiency of tracking errors. The designed control laws make the control system of USV semi-globally practically finite-time stable (SGPFS) and make the tracking errors tend to a narrow residual set involving the specified bound in a finite time. In the end, the simulations reflect the presented FFTC’s efficiency.https://ieeexplore.ieee.org/document/10374356/Unmanned surface vesseltracking controlevent-triggered controlprescribed performancefinite-time control |
| spellingShingle | Yuan Liu Li Zhao Wenfang Sun Qing Wang Guoxing Li Haiyang Guo Xinxiang Zhang Jiaming Zhang Zhiqing Bai Event-Triggered Finite-Time Tracking Control of Unmanned Surface Vessel Using Neural Network Prescribed Performance IEEE Access Unmanned surface vessel tracking control event-triggered control prescribed performance finite-time control |
| title | Event-Triggered Finite-Time Tracking Control of Unmanned Surface Vessel Using Neural Network Prescribed Performance |
| title_full | Event-Triggered Finite-Time Tracking Control of Unmanned Surface Vessel Using Neural Network Prescribed Performance |
| title_fullStr | Event-Triggered Finite-Time Tracking Control of Unmanned Surface Vessel Using Neural Network Prescribed Performance |
| title_full_unstemmed | Event-Triggered Finite-Time Tracking Control of Unmanned Surface Vessel Using Neural Network Prescribed Performance |
| title_short | Event-Triggered Finite-Time Tracking Control of Unmanned Surface Vessel Using Neural Network Prescribed Performance |
| title_sort | event triggered finite time tracking control of unmanned surface vessel using neural network prescribed performance |
| topic | Unmanned surface vessel tracking control event-triggered control prescribed performance finite-time control |
| url | https://ieeexplore.ieee.org/document/10374356/ |
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