Model-free adaptive control for unmanned surface vessels: a literature review
Model-Free Adaptive Control (MFAC) is a control strategy that eliminates the need for prior knowledge of the system model by leveraging online data to learn the system dynamics and design controllers. This paper offers a comprehensive exploration of the significance of control theory in unmanned sur...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Taylor & Francis Group
2024-12-01
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| Series: | Systems Science & Control Engineering |
| Subjects: | |
| Online Access: | https://www.tandfonline.com/doi/10.1080/21642583.2024.2316170 |
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| Summary: | Model-Free Adaptive Control (MFAC) is a control strategy that eliminates the need for prior knowledge of the system model by leveraging online data to learn the system dynamics and design controllers. This paper offers a comprehensive exploration of the significance of control theory in unmanned surface vessels (USVs), with a particular focus on data-driven approaches. It provides a comprehensive overview of various MFAC algorithms proposed for USVs in diverse scenarios, including neural network-based MFAC, reinforcement learning-based MFAC, and fuzzy logic-based MFAC. The objective of this review is to provide a profound understanding of the latest advancements in MFAC technologies and serve as a guiding resource for further developments in the field. |
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| ISSN: | 2164-2583 |