Improved Long Short-Term Memory-Based Fixed-Time Fault-Tolerant Control for Unmanned Marine Vehicles with Signal Quantization
This paper presents a fixed-time fault-tolerant control strategy based on an improved long short-term memory network for dynamic positioning of unmanned marine vehicles subject to signal quantization, disturbances, and input saturation. Firstly, an improved long short-term memory network optimized b...
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| Published in | Journal of marine science and engineering Vol. 13; no. 10; p. 2012 |
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| Main Authors | , , , , |
| Format | Journal Article |
| Language | English |
| Published |
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MDPI AG
20.10.2025
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| Online Access | Get full text |
| ISSN | 2077-1312 2077-1312 |
| DOI | 10.3390/jmse13102012 |
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| Abstract | This paper presents a fixed-time fault-tolerant control strategy based on an improved long short-term memory network for dynamic positioning of unmanned marine vehicles subject to signal quantization, disturbances, and input saturation. Firstly, an improved long short-term memory network optimized by an adaptive mixed-gradient algorithm is developed to accurately estimate external disturbances. Secondly, a fixed-time extended state observer is designed to rapidly predict thruster faults. Subsequently, within a fixed-time control framework, a novel terminal sliding-mode surface incorporating signal quantization parameters is constructed. In addition, a dynamic uniform quantization strategy with tunable sensitivity is introduced to effectively alleviate the performance degradation induced by quantization errors. Based on this, a fixed-time fault-tolerant controller is constructed. Finally, simulation results and comparative experiments are provided to demonstrate the effectiveness of the proposed control scheme. |
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| AbstractList | This paper presents a fixed-time fault-tolerant control strategy based on an improved long short-term memory network for dynamic positioning of unmanned marine vehicles subject to signal quantization, disturbances, and input saturation. Firstly, an improved long short-term memory network optimized by an adaptive mixed-gradient algorithm is developed to accurately estimate external disturbances. Secondly, a fixed-time extended state observer is designed to rapidly predict thruster faults. Subsequently, within a fixed-time control framework, a novel terminal sliding-mode surface incorporating signal quantization parameters is constructed. In addition, a dynamic uniform quantization strategy with tunable sensitivity is introduced to effectively alleviate the performance degradation induced by quantization errors. Based on this, a fixed-time fault-tolerant controller is constructed. Finally, simulation results and comparative experiments are provided to demonstrate the effectiveness of the proposed control scheme. |
| Author | Yang, Xin Wang, Jia-Bin Li, Tieshan Hao, Li-Ying Dong, Gege |
| Author_xml | – sequence: 1 givenname: Xin surname: Yang fullname: Yang, Xin – sequence: 2 givenname: Li-Ying orcidid: 0000-0003-4721-3671 surname: Hao fullname: Hao, Li-Ying – sequence: 3 givenname: Jia-Bin surname: Wang fullname: Wang, Jia-Bin – sequence: 4 givenname: Gege surname: Dong fullname: Dong, Gege – sequence: 5 givenname: Tieshan surname: Li fullname: Li, Tieshan |
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| SubjectTerms | Adaptive algorithms Controllers Disturbances Dynamic positioning Fault tolerance Long short-term memory Neural networks Optimization algorithms Performance degradation Sliding mode control State observers Thrusters Unmanned vehicles Vehicles |
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| Title | Improved Long Short-Term Memory-Based Fixed-Time Fault-Tolerant Control for Unmanned Marine Vehicles with Signal Quantization |
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