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Open AccessArticle
LSTM-Based Predefined-Time Model Predictive Tracking Control for Unmanned Surface Vehicles with Disturbance and Actuator Faults
by
Yuxing Zhou
Yuxing Zhou ,
Li-Ying Hao
Li-Ying Hao *
and
Hudayberenov Atajan
Hudayberenov Atajan
Marine Electrical Engineering College, Dalian Maritime University, Dalian 116026, China
*
Author to whom correspondence should be addressed.
J. Mar. Sci. Eng. 2025, 13(10), 1914; https://doi.org/10.3390/jmse13101914 (registering DOI)
Submission received: 12 September 2025
/
Revised: 29 September 2025
/
Accepted: 3 October 2025
/
Published: 5 October 2025
Abstract
Predefined-time control has been extensively implemented in marine control systems due to its capability to enhance transient performance and achieve superior control specifications. However, inaccurate control execution resulting from faulty actuators can compromise this control strategy and critically undermine system performance. To address this challenge, this paper propose a predefined-time model predictive fault-tolerant control strategy for unmanned surface vessels (USVs) while considering actuator failures and ocean disturbances. Firstly, a novel predefined-time model predictive control (PTMPC) strategy is designed by incorporating contraction constraints derived from an auxiliary predefined-time control system into the proposed optimization framework. This ensures that the resulting control variables guarantee predefined-time convergence of tracking errors when applied to the USV system. Furthermore, a long short-term memory-based neural network for disturbance prediction is integrated into the control strategy, leveraging its exceptional capability in modeling temporal sequences to achieve accurate forecasting of ocean disturbances. Thirdly, the proposed control scheme utilizes its integrated fault observation mechanism to actively compensate for actuator failures through real-time fault estimation, ensuring predefined-time convergence performance while providing rigorous guarantees of closed-loop stability and feasibility. Finally, simulation results demonstrate the efficacy and superiority of the proposed algorithm.
Share and Cite
MDPI and ACS Style
Zhou, Y.; Hao, L.-Y.; Atajan, H.
LSTM-Based Predefined-Time Model Predictive Tracking Control for Unmanned Surface Vehicles with Disturbance and Actuator Faults. J. Mar. Sci. Eng. 2025, 13, 1914.
https://doi.org/10.3390/jmse13101914
AMA Style
Zhou Y, Hao L-Y, Atajan H.
LSTM-Based Predefined-Time Model Predictive Tracking Control for Unmanned Surface Vehicles with Disturbance and Actuator Faults. Journal of Marine Science and Engineering. 2025; 13(10):1914.
https://doi.org/10.3390/jmse13101914
Chicago/Turabian Style
Zhou, Yuxing, Li-Ying Hao, and Hudayberenov Atajan.
2025. "LSTM-Based Predefined-Time Model Predictive Tracking Control for Unmanned Surface Vehicles with Disturbance and Actuator Faults" Journal of Marine Science and Engineering 13, no. 10: 1914.
https://doi.org/10.3390/jmse13101914
APA Style
Zhou, Y., Hao, L.-Y., & Atajan, H.
(2025). LSTM-Based Predefined-Time Model Predictive Tracking Control for Unmanned Surface Vehicles with Disturbance and Actuator Faults. Journal of Marine Science and Engineering, 13(10), 1914.
https://doi.org/10.3390/jmse13101914
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