Next Article in Journal
Optimized Venturi-Ejector Adsorption Mechanism for Underwater Inspection Robots: Design, Simulation, and Field Testing
Previous Article in Journal
Development of Virtual Disk Method for Propeller Interacting with Free Surface
Previous Article in Special Issue
Research on Water Surface Object Detection Method Based on Image Fusion
 
 
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
This is an early access version, the complete PDF, HTML, and XML versions will be available soon.
Article

LSTM-Based Predefined-Time Model Predictive Tracking Control for Unmanned Surface Vehicles with Disturbance and Actuator Faults

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
(This article belongs to the Special Issue The Control and Navigation of Autonomous Surface Vehicles)

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.
Keywords: fault-tolerant control; model predictive control; long short-term memory; predefined-time control; unmanned surface vehicles; trajectory tracking fault-tolerant control; model predictive control; long short-term memory; predefined-time control; unmanned surface vehicles; trajectory tracking

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

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop