Fuzzy linear extended states observer‐based iteration learning fault‐tolerant control for autonomous underwater vehicle trajectory‐tracking system
Abstract To deal with thrusters’ faults of autonomous underwater vehicle (AUV), an iterative learning algorithm fault‐tolerant control (FTC) based on the linear extended states observer (LESO) is proposed. In this control scheme, the non‐linear feedback mechanism of the LESO is transplanted into ite...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2023-02-01
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| Series: | IET Control Theory & Applications |
| Online Access: | https://doi.org/10.1049/cth2.12288 |
| _version_ | 1797936347211628544 |
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| author | Chao Hou XiaoGang Li Hongbo Wang Peng Zhai Hao Lu |
| author_facet | Chao Hou XiaoGang Li Hongbo Wang Peng Zhai Hao Lu |
| author_sort | Chao Hou |
| collection | DOAJ |
| description | Abstract To deal with thrusters’ faults of autonomous underwater vehicle (AUV), an iterative learning algorithm fault‐tolerant control (FTC) based on the linear extended states observer (LESO) is proposed. In this control scheme, the non‐linear feedback mechanism of the LESO is transplanted into iterative learning processes to estimate fault. Compared to our previous work, LESO is used to substitute classic non‐linear extended state observer to make the establishment of the whole system more structured; moreover, the number of parameters need to be tuned can be reduced by the conception of observer bandwidth of LESO. To enhance the controllability and robustness of whole scheme, a new saturated sliding mode controller is proposed based on the Lyapunov theory. Then to achieve online parameter self‐tuning for the control system, fuzzy logic controllers are introduced to find optimal relationship between LESO's parameter and tracking errors. The performance of the proposed controller is tested by some comparison experiments on Zhuhai A18D AUV; the results show that the proposed control scheme can ensure better stability than classical control and our previous control scheme when AUV suffers faults. |
| first_indexed | 2024-04-10T18:28:21Z |
| format | Article |
| id | doaj.art-fbe3c4b86a734a97a0f6e0d0837c0e63 |
| institution | Directory Open Access Journal |
| issn | 1751-8644 1751-8652 |
| language | English |
| last_indexed | 2024-04-10T18:28:21Z |
| publishDate | 2023-02-01 |
| publisher | Wiley |
| record_format | Article |
| series | IET Control Theory & Applications |
| spelling | doaj.art-fbe3c4b86a734a97a0f6e0d0837c0e632023-02-02T05:08:33ZengWileyIET Control Theory & Applications1751-86441751-86522023-02-0117327028310.1049/cth2.12288Fuzzy linear extended states observer‐based iteration learning fault‐tolerant control for autonomous underwater vehicle trajectory‐tracking systemChao Hou0XiaoGang Li1Hongbo Wang2Peng Zhai3Hao Lu4Academy for Engineering & Technology Fudan University Shanghai ChinaCollege of Engineering Ocean University of China Shandong Engineering Research Center of Marine Intelligent Equipment and Instruments Qingdao ChinaAcademy for Engineering & Technology Fudan University Shanghai ChinaAcademy for Engineering & Technology Fudan University Shanghai ChinaAcademy for Engineering & Technology Fudan University Shanghai ChinaAbstract To deal with thrusters’ faults of autonomous underwater vehicle (AUV), an iterative learning algorithm fault‐tolerant control (FTC) based on the linear extended states observer (LESO) is proposed. In this control scheme, the non‐linear feedback mechanism of the LESO is transplanted into iterative learning processes to estimate fault. Compared to our previous work, LESO is used to substitute classic non‐linear extended state observer to make the establishment of the whole system more structured; moreover, the number of parameters need to be tuned can be reduced by the conception of observer bandwidth of LESO. To enhance the controllability and robustness of whole scheme, a new saturated sliding mode controller is proposed based on the Lyapunov theory. Then to achieve online parameter self‐tuning for the control system, fuzzy logic controllers are introduced to find optimal relationship between LESO's parameter and tracking errors. The performance of the proposed controller is tested by some comparison experiments on Zhuhai A18D AUV; the results show that the proposed control scheme can ensure better stability than classical control and our previous control scheme when AUV suffers faults.https://doi.org/10.1049/cth2.12288 |
| spellingShingle | Chao Hou XiaoGang Li Hongbo Wang Peng Zhai Hao Lu Fuzzy linear extended states observer‐based iteration learning fault‐tolerant control for autonomous underwater vehicle trajectory‐tracking system IET Control Theory & Applications |
| title | Fuzzy linear extended states observer‐based iteration learning fault‐tolerant control for autonomous underwater vehicle trajectory‐tracking system |
| title_full | Fuzzy linear extended states observer‐based iteration learning fault‐tolerant control for autonomous underwater vehicle trajectory‐tracking system |
| title_fullStr | Fuzzy linear extended states observer‐based iteration learning fault‐tolerant control for autonomous underwater vehicle trajectory‐tracking system |
| title_full_unstemmed | Fuzzy linear extended states observer‐based iteration learning fault‐tolerant control for autonomous underwater vehicle trajectory‐tracking system |
| title_short | Fuzzy linear extended states observer‐based iteration learning fault‐tolerant control for autonomous underwater vehicle trajectory‐tracking system |
| title_sort | fuzzy linear extended states observer based iteration learning fault tolerant control for autonomous underwater vehicle trajectory tracking system |
| url | https://doi.org/10.1049/cth2.12288 |
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