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Dynamics and Control of Marine Mechatronics
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Dynamics and Control of Marine Mechatronics

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 2025 | Viewed by 432

Special Issue Editor

Dr. Tao Wang

E-Mail Website
Guest Editor
Ocean College, Zhejiang University, Hangzhou 310000, China
Interests: mechatronics; marine robots; energy harvesting
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

In recent years, marine mechatronics has emerged as a cornerstone of modern ocean engineering, driven by the growing demand for intelligent, adaptive systems in offshore exploration, renewable energy harvesting, and underwater robotics. Integrating mechanics, electronics, and advanced control theory, this field addresses the unique challenges posed by highly nonlinear dynamics, multi-physical interactions, and unpredictable marine environments. Key research priorities include optimizing energy efficiency, ensuring operational reliability under extreme conditions, and enabling autonomous decision making for complex tasks such as deep-sea exploration or offshore infrastructure maintenance.

The design and control of marine mechatronic systems—from autonomous underwater vehicles (AUVs) to wave energy converters—require innovative solutions that balance precision, robustness, and cost-effectiveness. Traditional approaches, such as PID control or linearized models, often fall short in handling the inherent complexities of hydrodynamic–structure interactions, time-varying payloads, and sensor limitations. Emerging trends emphasize hybrid methodologies, combining model-based optimization with data-driven techniques like machine learning, to enhance system adaptability and fault tolerance.

This Special Issue on “Dynamics and Control of Marine Mechatronics” covers topics including, but not limited to, the following:

  • Advanced control architectures for marine robotic systems;
  • Modeling of coupled mechanical–electrical–hydrodynamic dynamics;
  • Real-time sensor fusion and state estimation in noisy or partially observable environments;
  • Energy-efficient actuation and power management for long-duration missions;
  • Resilient system design addressing failures, extreme loads, or environmental uncertainties;
  • AI-driven solutions for autonomous navigation, path planning, and swarm coordination.

Dr. Tao Wang
Guest Editor

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

  • marine mechatronics
  • dynamics modeling
  • advanced control
  • AI-driven solutions
  • energy-efficient actuation
  • marine energy harvesting
  • marine robot
  • intelligent system

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Published Papers (2 papers)

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32 pages, 5154 KiB  
Article
A Hierarchical Reinforcement Learning Framework for Multi-Agent Cooperative Maneuver Interception in Dynamic Environments
by Qinlong Huang, Yasong Luo, Zhong Liu, Jiawei Xia, Ming Chang and Jiaqi Li
J. Mar. Sci. Eng. 2025, 13(7), 1271; https://doi.org/10.3390/jmse13071271 - 29 Jun 2025
Viewed by 158
Abstract
To address the challenges of real-time decision-making and resource optimization in multi-agent cooperative interception tasks within dynamic environments, this paper proposes a hierarchical framework for reinforcement learning-based interception algorithm (HFRL-IA). By constructing a hierarchical Markov decision process (MDP) model based on dynamic game [...] Read more.
To address the challenges of real-time decision-making and resource optimization in multi-agent cooperative interception tasks within dynamic environments, this paper proposes a hierarchical framework for reinforcement learning-based interception algorithm (HFRL-IA). By constructing a hierarchical Markov decision process (MDP) model based on dynamic game equilibrium theory, the complex interception task is decomposed into two hierarchically optimized stages: dynamic task allocation and distributed path planning. At the high level, a sequence-to-sequence reinforcement learning approach is employed to achieve dynamic bipartite graph matching, leveraging a graph neural network encoder–decoder architecture to handle dynamically expanding threat targets. At the low level, an improved prioritized experience replay multi-agent deep deterministic policy gradient algorithm (PER-MADDPG) is designed, integrating curriculum learning and prioritized experience replay mechanisms to effectively enhance the interception success rate against complex maneuvering targets. Extensive simulations in diverse scenarios and comparisons with conventional task assignment strategies demonstrate the superiority of the proposed algorithm. Taking a typical scenario of 10 agents intercepting as an example, the HFRL-IA algorithm achieves a 22.51% increase in training rewards compared to the traditional end-to-end MADDPG algorithm, and the interception success rate is improved by 26.37%. This study provides a new methodological framework for distributed cooperative decision-making in dynamic adversarial environments, with significant application potential in areas such as maritime multi-agent security defense and marine environment monitoring. Full article
(This article belongs to the Special Issue Dynamics and Control of Marine Mechatronics)
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26 pages, 3671 KiB  
Article
Energy-Optimized Path Planning for Fully Actuated AUVs in Complex 3D Environments
by Shuo Liu, Zhengfei Wang, Tao Wang, Shanmin Zhou, Yu Zhang, Pengji Jin and Guanjun Yang
J. Mar. Sci. Eng. 2025, 13(7), 1269; https://doi.org/10.3390/jmse13071269 - 29 Jun 2025
Viewed by 107
Abstract
This paper presents an energy-optimized path planning approach for fully actuated autonomous underwater vehicles (AUVs) in three-dimensional ocean environments to enhance their operational range and endurance. A fully actuated AUV is characterized by its high degrees of freedom and precise controllability. Using real [...] Read more.
This paper presents an energy-optimized path planning approach for fully actuated autonomous underwater vehicles (AUVs) in three-dimensional ocean environments to enhance their operational range and endurance. A fully actuated AUV is characterized by its high degrees of freedom and precise controllability. Using real terrain data, we construct environmental models incorporating a Lamb vortex and random obstacles. We develop a mathematical model of the AUV’s total energy consumption, accounting for constraints imposed by its fully actuated design and extensive maneuverability. To minimize energy usage, we propose an energy-optimized path planning algorithm that combines energy-optimized particle swarm optimization (EOPSO) and sequential quadratic programming (SQP). The proposed method identifies the optimal path for energy consumption and the corresponding optimal surge speed. The efficacy of the algorithm in optimizing the total energy consumption of the AUV is demonstrated through the simulation of various scenarios. In comparison to other algorithms, paths planned by this algorithm are shown to have superior robustness and optimized energy consumption. Full article
(This article belongs to the Special Issue Dynamics and Control of Marine Mechatronics)
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