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System Optimization and Control of Unmanned Marine Vehicles
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System Optimization and Control of Unmanned Marine Vehicles

A special issue of Journal of Marine Science and Engineering (ISSN 2077-1312). This special issue belongs to the section "Ocean Engineering".

Deadline for manuscript submissions: 1 October 2026 | Viewed by 3317

Special Issue Editors

Prof. Dr. Liying Hao

E-Mail Website
Guest Editor
College of Marine Electrical Engineering, Dalian Maritime University, Dalian 116026, China
Interests: robust fault-tolerant control; sliding-mode control; model predictive control; deep learning with an emphasis on applications in marine vehicles
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Weidong Zhang

E-Mail Website
Guest Editor
Department of Automation, Shanghai Jiao Tong University, Shanghai 200240, China
Interests: marine vehicles; robust control; intelligent control; guidance; nonlinear control
Special Issues, Collections and Topics in MDPI journals
Dr. Yongpeng Weng

E-Mail Website
Guest Editor
College of Marine Electrical Engineering, Dalian Maritime University, Dalian 116026, China
Interests: data-driven control; fault-tolerant control; unmanned surface vehicles; unmanned aerial vehicles

Special Issue Information

Dear Colleagues,

Unmanned marine vehicles are revolutionizing ocean operations ranging from environmental monitoring to offshore infrastructure inspection, driving critical demand for advanced system optimization to ensure safer, and sustainable marine operations. Recent breakthroughs in optimization theory, artificial intelligence, and marine systems engineering provide critical tools for guidance, navigation and control, energy management, and communications in harsh ocean environments.

This special issue will investigate advanced optimization methodologies for enhancing the performance, autonomy, and operational efficiency of unmanned marine vehicles in complex ocean environments. Specifically, we invite studies focusing on innovative approaches to system optimization, including but not limited to reinforcement learning-based control, adaptive control strategies, multi-agent consensus control, and anti-saturation control. Research should demonstrate how these optimization techniques improve mission capabilities, endurance, or decision-making processes in real-world marine applications.

Prof. Dr. Liying Hao
Prof. Dr. Weidong Zhang
Dr. Yongpeng Weng
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Journal of Marine Science and Engineering is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • multi-objective design and optimization
  • distributed formation control
  • path planning and dynamic positioning
  • navigation and optimal control
  • fault diagnosis and cyber secure optimization
  • perception and motion planning
  • sliding mode fault-tolerant control
  • sensor fusion and state estimation
  • model predictive control
  • system identification and parameter estimation
  • energy aware mission planning
  • digital twin validation
  • deep learning for marine vehicle control

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (3 papers)

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Research

23 pages, 7083 KB  
Article
An Improved Factor Graph Optimization Algorithm Enhanced with ANFIS for Ship GNSS/DR Integrated Navigation
by Yi Jiang, Heng Gao, Tianyu Zhang, Jin Xiang, Yichi Zhang, Jingqing Ke and Qing Hu
J. Mar. Sci. Eng. 2026, 14(5), 472; https://doi.org/10.3390/jmse14050472 - 28 Feb 2026
Viewed by 567
Abstract
Accurate and reliable positioning is essential for unmanned marine vehicles (UMVs), especially in complex maritime environments. Existing algorithms often underutilize historical information, struggle with nonlinear dynamics, and lack adaptability in the GNSS Measurement Noise Covariance, leading to degraded performance. This study proposes an [...] Read more.
Accurate and reliable positioning is essential for unmanned marine vehicles (UMVs), especially in complex maritime environments. Existing algorithms often underutilize historical information, struggle with nonlinear dynamics, and lack adaptability in the GNSS Measurement Noise Covariance, leading to degraded performance. This study proposes an enhanced Factor Graph Optimization (FGO) method integrated with an adaptive neuro-fuzzy inference system (ANFIS) to overcome these challenges. First, an improved GNSS/Dead Reckoning (DR) factor graph is built using refined error models to enhance baseline accuracy. Second, a marginalization factor is introduced utilizing a sliding window and the Schur complement method to retain informative historical data while reducing computational load, thereby improving stability and field performance. Third, an ANFIS-based adaptive GNSS factor dynamically updates the GNSS Measurement Noise Covariance Matrix (GMNCM) to strengthen robustness under variable maritime conditions. Simulation and field tests demonstrate significant improvements: the proposed method achieves 29.1%, 26.5%, and 9.9% higher accuracy than EKF, UKF, and conventional FGO, respctively. Under GNSS interruptions, EKF and UKF diverge with errors exceeding 500 m, while FGO limits drift to 20 m. The proposed ANFIS–FGO shows the smallest fluctuations and fastest recovery, confirming its strong resilience and practical applicability for UMV navigation. Full article
(This article belongs to the Special Issue System Optimization and Control of Unmanned Marine Vehicles)
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40 pages, 8354 KB  
Article
System-Level Optimization of AUV Swarm Control and Perception: An Energy-Aware Federated Meta-Transfer Learning Framework with Digital Twin Validation
by Zinan Nie, Hongjun Tian, Yijie Yin, Yuhan Zhou, Wei Li, Yang Xiong, Yichen Wang, Zitong Zhang, Yang Yang, Dongxiao Xie, Manlin Wang and Shijie Huang
J. Mar. Sci. Eng. 2026, 14(4), 384; https://doi.org/10.3390/jmse14040384 - 18 Feb 2026
Cited by 1 | Viewed by 774
Abstract
Deep-sea exploration increasingly relies on Autonomous Underwater Vehicles (AUVs) to enable persistent, wide-area surveying in harsh and uncertain environments. In practice, however, deployments are constrained by tight energy budgets and bandwidth-limited, intermittent acoustic links, which complicate mission-level coordination. Moreover, many existing systems treat [...] Read more.
Deep-sea exploration increasingly relies on Autonomous Underwater Vehicles (AUVs) to enable persistent, wide-area surveying in harsh and uncertain environments. In practice, however, deployments are constrained by tight energy budgets and bandwidth-limited, intermittent acoustic links, which complicate mission-level coordination. Moreover, many existing systems treat perception and control as loosely coupled modules, often resulting in redundant sensing, inefficient communication, and degraded overall performance—particularly under heterogeneous sensing modalities and shifting geological conditions. To address these challenges, we propose a hierarchical Federated Meta-Transfer Learning (FMTL) framework that tightly integrates collaborative perception with adaptive control for swarm optimization. The framework operates at three levels: (1) Representation Learning aligns heterogeneous sensors in a shared latent space via a physics-informed contrastive objective, substantially reducing communication overhead; (2) Meta-Learning Adaptation enables rapid transfer and convergence in new environments with minimal data exchange; and (3) Energy-Aware Control realizes closed-loop exploration by coupling Federated Explainable AI (FXAI) with decentralized multi-agent reinforcement learning (MARL) for path planning under energy constraints. Validated in high-fidelity hardware-in-the-loop simulations and a digital-twin environment, FMTL outperforms state-of-the-art baselines, achieving an AUC of 0.94 for target identification. Furthermore, an energy–intelligence Pareto analysis demonstrates a 4.5× improvement in information gain per Joule. Overall, this work provides a physically consistent and communication-efficient blueprint for the optimization and control of next-generation intelligent marine swarms. Full article
(This article belongs to the Special Issue System Optimization and Control of Unmanned Marine Vehicles)
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23 pages, 2792 KB  
Article
Improved Long Short-Term Memory-Based Fixed-Time Fault-Tolerant Control for Unmanned Marine Vehicles with Signal Quantization
by Xin Yang, Li-Ying Hao, Jia-Bin Wang, Gege Dong and Tieshan Li
J. Mar. Sci. Eng. 2025, 13(10), 2012; https://doi.org/10.3390/jmse13102012 - 20 Oct 2025
Viewed by 537
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 [...] Read more.
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. Full article
(This article belongs to the Special Issue System Optimization and Control of Unmanned Marine Vehicles)
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