Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
4.4
2.7
Journals
JMSE
Special Issues
Advanced Control Strategies for Autonomous Maritime Systems
share announcement

Advanced Control Strategies for Autonomous Maritime Systems

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: 30 September 2025 | Viewed by 4270

Special Issue Editors

Dr. Hossein Nejatbakhsh Esfahani

E-Mail Website
Guest Editor
Department of Mechanical Engineering, Clemson University, Clemson, SC, USA
Interests: robust/optimal control systems; safety-critical control; model predictive control; reinforcement learning; autonomous and intelligent platforms (unmanned aerial and marine vehicles)
Dr. Arash Bahari Kordabad

E-Mail Website
Guest Editor
Max Planck Institute for Software Systems, 67663 Kaiserslautern, Germany
Interests: safe reinforcement learning; model predictive control; optimization for autonomous vehicles; smart-grid applications; multi-agent systems
Prof. Dr. David Moreno-Salinas

E-Mail Website
Guest Editor
Department of Computer Science and Automatic Control, National Distance Education University (UNED), 28040 Madrid, Spain
Interests: localization; control; sensor networks; marine vehicles; identification and modelling
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Autonomous Maritime Systems (AMS) are strongly connected to the maritime industry, with applications ranging from unmanned surface and underwater vehicles to intelligent shipping solutions. Advanced path planning and control algorithms are vital for ensuring that these unmanned vehicles can operate autonomously with high reliability and precision, especially in challenging maritime environments. These control approaches incorporate various elements of adaptive and robust machine learning methods and optimal control schemes, which allow AMS to safely navigate in the presence of external disturbances such as waves, ocean currents, and wind. By using advanced learning-based optimal control strategies, AMS can optimize their trajectory, minimize fuel consumption, and improve overall operational efficiency. These control methods also account for the inherent uncertainties in the maritime domain, ensuring system robustness against disturbances and equipment failures. In addition to basic navigation and collision avoidance, advanced control strategies enable higher-level autonomy for networked autonomous marine vehicles. For example, multi-agent systems coordination allows fleets of autonomous ships or underwater vehicles to collaborate on complex missions. This Special Issue offers a collection of high-quality research articles contributing to topics on:

  • Guidance, navigation, and control of autonomous surface and underwater vehicles;
  • Path following, path planning, and collision avoidance algorithms;
  • Methods and tools for the development of digital twins of marine control systems;
  • Risk-aware decision-making and safety-critical control of autonomous marine systems;
  • Intelligent autonomous marine systems;
  • Learning-based control algorithms for marine robotics;
  • Robust, adaptive, and nonlinear control approaches in marine systems;
  • Model Predictive Control (MPC) for motion planning and control of autonomous marine vehicles;
  • Distributed and cooperative control systems for marine operations.

Dr. Hossein Nejatbakhsh Esfahani
Dr. Arash Bahari Kordabad
Prof. Dr. David Moreno-Salinas
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 100 words) can be sent to the Editorial Office for announcement on this website.

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 monthly 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

  • unmanned surface and underwater vehicles
  • guidance, navigation and control (GNC) algorithms
  • path planning and collision avoidance
  • robust and adaptive nonlinear control
  • intelligent and learning-based control
  • reinforcement learning methods and optimal control
  • advanced model predictive control schemes
  • cooperative and networked control
  • risk-aware and safety-critical control

Benefits of Publishing in a Special Issue

  • Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • e-Book format: Special Issues with more than 10 articles can be published as dedicated e-books, ensuring wide and rapid dissemination.

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

Published Papers (5 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

20 pages, 1076 KiB  
Article
Passivity-Based Sliding Mode Control for the Robust Trajectory Tracking of Unmanned Surface Vessels Under External Disturbances and Model Uncertainty
by Luke Ma, Siyi Pang, Yao He, Yongxin Wu, Yanjun Li and Weijun Zhou
J. Mar. Sci. Eng. 2025, 13(2), 364; https://doi.org/10.3390/jmse13020364 - 16 Feb 2025
Viewed by 452
Abstract
This study uses a port-Hamiltonian framework to address trajectory tracking control for unmanned surface vessels (USVs) under unknown disturbances. A passivity-based sliding mode controller is designed, integrating adaptive disturbance estimation and an RBFNN-based uncertainty estimator. Stability is rigorously proven, and simulations confirm superior [...] Read more.
This study uses a port-Hamiltonian framework to address trajectory tracking control for unmanned surface vessels (USVs) under unknown disturbances. A passivity-based sliding mode controller is designed, integrating adaptive disturbance estimation and an RBFNN-based uncertainty estimator. Stability is rigorously proven, and simulations confirm superior tracking performance, strong disturbance rejection, and accurate uncertainty estimation. Full article
(This article belongs to the Special Issue Advanced Control Strategies for Autonomous Maritime Systems)
►▼ Show Figures

Figure 1

21 pages, 2523 KiB  
Article
Networked Predictive Trajectory Tracking Control for Underactuated USV with Time-Varying Delays
by Tao Lei, Yuanqiao Wen, Yi Yu, Minglong Zhang, Xin Xiong and Kang Tian
J. Mar. Sci. Eng. 2025, 13(1), 132; https://doi.org/10.3390/jmse13010132 - 13 Jan 2025
Cited by 1 | Viewed by 754
Abstract
This study explores the control framework for the trajectory tracking problem concerning unmanned surface vessels (USVs) in the presence of time-varying communication delays. To address the aforementioned problem, a novel networked predictive sliding mode control architecture is proposed by integrating a discrete sliding [...] Read more.
This study explores the control framework for the trajectory tracking problem concerning unmanned surface vessels (USVs) in the presence of time-varying communication delays. To address the aforementioned problem, a novel networked predictive sliding mode control architecture is proposed by integrating a discrete sliding mode control technique and predictive control scheme. By leveraging a first-order forward Euler discretization approach, a discrete-time model of USVs was initially formulated. Then, a virtual velocity controller was developed to convert the position tracking into expected velocity tracking, which was achieved by utilizing a sliding mode control. Subsequently, a networked predictive control technique was performed to compensate for the time-varying delays. Finally, theoretical analysis and extensive comparative simulation tests demonstrated that the proposed control scheme guaranteed complete compensation for time-varying delays while ensuring the stability of the closed-loop system. Full article
(This article belongs to the Special Issue Advanced Control Strategies for Autonomous Maritime Systems)
►▼ Show Figures

Figure 1

16 pages, 2102 KiB  
Article
Advanced Control for Shipboard Cranes with Asymmetric Output Constraints
by Mingxuan Cao, Meng Xu, Yongqiao Gao, Tianlei Wang, Anan Deng and Zhenyu Liu
J. Mar. Sci. Eng. 2025, 13(1), 91; https://doi.org/10.3390/jmse13010091 - 6 Jan 2025
Viewed by 634
Abstract
Considering the anti-swing control and output constraint problems of shipboard cranes, a nonlinear anti-swing controller based on asymmetric barrier Lyapunov functions (BLFs) is designed. First, model transformation mitigates the explicit effects of ship roll on the desired position and payload fluctuations. Then, a [...] Read more.
Considering the anti-swing control and output constraint problems of shipboard cranes, a nonlinear anti-swing controller based on asymmetric barrier Lyapunov functions (BLFs) is designed. First, model transformation mitigates the explicit effects of ship roll on the desired position and payload fluctuations. Then, a newly constructed BLF is introduced into the energy-based Lyapunov candidate function to generate nonlinear displacement and angle constraint terms to control the rope length and boom luffing angle. Among these, constraints with positive bounds are effectively handled by the proposed BLF. For the swing constraints of the unactuated payload, a carefully designed relevant constraint term is embedded in the controller by constructing an auxiliary signal, and strict theoretical analysis is provided by using a reductio ad absurdum argument. Additionally, the auxiliary signal effectively couples the boom and payload motions, thereby improving swing suppression performance. Finally, the asymptotic stability is proven using LaSalle’s invariance principle. The simulation comparison results indicate that the proposed method exhibits satisfactory performance in swing suppression control and output constraints. In all simulation cases, the payload swing angle complies with the 3° constraint and converges to the desired range within 6 s. This study provides an effective solution to the control challenges of shipboard crane systems operating in confined spaces, offering significant practical value and applicability. Full article
(This article belongs to the Special Issue Advanced Control Strategies for Autonomous Maritime Systems)
►▼ Show Figures

Figure 1

25 pages, 4284 KiB  
Article
Reliable, Energy-Optimized, and Void-Aware (REOVA), Routing Protocol with Strategic Deployment in Mobile Underwater Acoustic Communications
by Muhammad Umar Khan, Muhammad Aamir and Pablo Otero
J. Mar. Sci. Eng. 2024, 12(12), 2215; https://doi.org/10.3390/jmse12122215 - 2 Dec 2024
Viewed by 804
Abstract
The Underwater Acoustic Sensor Networks have gained significant attention because of their wide range of applications in submerged environments. However, ensuring reliable and energy-efficient communication in the submerged environment is challenging due to their distinctive characteristics such as limited energy resources, dynamic topology, [...] Read more.
The Underwater Acoustic Sensor Networks have gained significant attention because of their wide range of applications in submerged environments. However, ensuring reliable and energy-efficient communication in the submerged environment is challenging due to their distinctive characteristics such as limited energy resources, dynamic topology, extended propagation delays, and node mobility. Additionally, the void hole problem in submerged environments arises due to randomized node deployment. To curtail these issues, this paper introduces a novel way of strategically deploying the nodes based on the underwater depth parameters, which can reduce the likelihood of void hole occurrence. An optimal number of clusters based on the fixed transmission range of cluster heads is used to cater to extensive energy usage. In the proposed routing protocol, the path selection is based on the residual energy, link quality, and proximity to a higher number of nodes. Extensive simulations have been conducted by varying network parameters to analyze the network performance in terms of energy expenditure, packet delivery ratio, network throughput, number of dead nodes, and end-to-end delays. Also, the proposed work provides a performance comparison with some state-of-the-art protocols and exhibits promising results. Full article
(This article belongs to the Special Issue Advanced Control Strategies for Autonomous Maritime Systems)
►▼ Show Figures

Figure 1

14 pages, 1401 KiB  
Article
An Offline Reinforcement Learning Approach for Path Following of an Unmanned Surface Vehicle
by Zexing Zhou, Tao Bao, Jun Ding, Yihong Chen, Zhengyi Jiang and Bo Zhang
J. Mar. Sci. Eng. 2024, 12(12), 2173; https://doi.org/10.3390/jmse12122173 - 28 Nov 2024
Cited by 1 | Viewed by 887
Abstract
Path following is crucial for enhancing the autonomy of unmanned surface vehicles (USVs) in water monitoring missions. This paper presents an offline reinforcement learning (RL) controller for USVs. The controller employs the soft actor–critic algorithm with a diversified Q-ensemble to optimize the steering [...] Read more.
Path following is crucial for enhancing the autonomy of unmanned surface vehicles (USVs) in water monitoring missions. This paper presents an offline reinforcement learning (RL) controller for USVs. The controller employs the soft actor–critic algorithm with a diversified Q-ensemble to optimize the steering control policy using a pre-collected dataset of USV path-following trials. A Markov decision process (MDP) tailored for path following is formulated. The proposed offline RL steering controller, trained on static datasets, demonstrates improved sample efficiency and asymptotic performance due to an expanded ensemble of Q-networks. The accuracy and adaptive learning capabilities of the RL controller are validated through simulations and free-running tests. Full article
(This article belongs to the Special Issue Advanced Control Strategies for Autonomous Maritime Systems)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-5
Back to TopTop