The formation control of autonomous surface vessels presents significant challenges when operating in close proximity, where ship-to-ship interaction becomes non-negligible. While conventional formation control methods often neglect these interactions or simplify them excessively, this paper develops a centralized model predictive control (MPC) framework that explicitly incorporates a three-degrees-of-freedom interaction model. This interaction model is constructed empirically based on existing computational fluid dynamics results, offering an efficient and practical way to approximate proximity-induced forces in real-time. The proposed control strategy enables accurate trajectory tracking and effective disturbance adaptation in typical formation geometries, including platooning, parallel, and triangular formations. Simulation results demonstrate that the MPC controller can outperform traditional PID controllers in both tracking precision and interaction robustness across the configurations. Formation-specific performance differences are also analyzed in detail.

Existing studies on multi-vessel formations rarely combine physically based models of ship–ship hydrodynamic interaction with online formation control, so that energy benefits are typically assessed offline or only approximated through artificial potentials. This paper addresses this gap by embedding a reduced-order, hydrodynamics-aware resistance model into a hierarchical formation control framework for multi vessel systems. A three degree of freedom interaction model is incorporated into the cost function, enabling the supervisory controller to adaptively optimize inter ship spacing and formation geometry in a speed dependent and hydrodynamics aware manner. The lower level MPC ensures accurate trajectory tracking and stability under the guidance of the top level optimization. Four simulation studies are conducted to evaluate the proposed method. The platooning formation is first analyzed as a reference, followed by the triangular formation, which achieves balanced tracking performance and stability. The echelon formation is then examined, demonstrating significant energy savings in medium to high speed regimes while maintaining yaw stability. Finally, an unconstrained optimization scenario is explored, where the system autonomously adapts its geometry without prescribed patterns, revealing emergent energy efficient and stable arrangements across different speed ranges. Results show that the proposed approach not only reduces resistance and improves energy efficiency but also enhances formation adaptability and robustness under varying operating conditions. These findings provide new insights into hydrodynamics aware cooperative control and the development of energy conscious fleet management strategies for future maritime transportation.

This paper presents a stochastic optimization model for predictive maintenance scheduling in offshore wind farms. The proposed model integrates probabilistic Remaining Useful Life (RUL) prognosis with mathematical optimization and Model Predictive Control (MPC) techniques that updates RUL beliefs with new prognostic measurements at each epoch to dynamically adjust maintenance decisions. Unlike conventional scheduling methods that rely on static age thresholds, our approach uses real-time prognostics to improve cost efficiency and reduce downtime. A case study on 50 wind turbines demonstrates that dynamically adapting maintenance schedules using prognostics reduces O&M expenses by 8.7%, primarily through significant reductions in downtime, compared to traditional methods.

System identification of full-scale surface vessels must address significant uncertainties arising from model mismatch, sensor noise, and environmental disturbances. To provide safety, robustness, and constraint satisfaction guarantees, especially for autonomous navigation applications, it is essential to quantify the bounds of parametric model uncertainty. This paper proposes a set-membership identification method for estimating key parameters of a nonlinear vessel maneuvering model, including inertia and added-mass terms, other hydrodynamic derivatives in the Coriolis-centripetal, damping matrices, and actuation-related parameters. The method provides a bounded-error characterisation of uncertainties, offering a reliable framework for modelling the effects of measurement noise, wind, and waves. It involves computing a data-driven parameter set (DDPS) using input-output measurements and model assumptions, which is further used to compute a feasible parameter set (FPS). The parameter estimates are then obtained by iteratively solving a quadratic program over the FPS polytope. Validation of the method using experimental data from a full-scale catamaran demonstrates improved accuracy of up to 26.5% as compared to existing approaches, significantly faster computational times, and its capability to provide bounded parameter estimates.

This paper presents a distributed solution for the problem of collaborative collision avoidance for autonomous inland waterway ships. A two-layer collision avoidance framework that considers inland waterway traffic regulations is proposed to increase navigational safety for autonomous ships. Our approach allows for modifying traffic rules without changing the collision avoidance algorithm, which is based on a novel formulation of model predictive control (MPC) for collision avoidance of ships. This MPC formulation is designed for inland waterway traffic and can handle complex scenarios. The alternating direction method of multipliers (ADMM) is used as a scheme for exchanging and negotiating intentions among ships. Simulation results demonstrate that the proposed algorithm enables ships to avoid collisions with a sufficient margin while adhering to traffic rules. Furthermore, the proposed algorithm can safely deviate from traffic rules when necessary to increase traffic efficiency in complex scenarios.

Over the past decade, autonomous surface vessels (ASVs) have increasingly operated in a range of challenging environments involving safety-critical scenarios. Their navigational capabilities rely on rich and reliable sensor data, enabling accurate localisation, situational awareness and environmental perception. This allows ASVs to perform motion planning, collision avoidance and navigational control tasks. To ensure maritime safety, faults affecting onboard navigational sensors must be diagnosed. This paper presents a model-based fault diagnosis scheme for ASVs affected by multiple sensor faults. Model-based methods utilise available sensors and dynamical models for residual generation. However, models describing the navigation may vary considerably for ASVs due to differences in vessel types, actuator configurations and sensor setups. To address this challenge, multiple residuals are synthesised using observer-based monitoring modules in the navigational sensors. Considering the impact of uncertainties, the residuals are designed to be bounded by adaptive thresholds proposed for each monitoring module. Fault isolation is then performed using a combinatorial decision logic, achieved by grouping the available sensors into multiple sensor sets and supported by model-based sensitivity analysis. Finally, the effectiveness of the proposed scheme is verified through simulation examples of two real-world vessels of different types with different sensor and actuator configurations, thereby illustrating its application.

Maritime decarbonization and the emergence of autonomous vessel fleets are motivating cooperative sailing concepts in which formation geometry serves not only as a coordination variable but also as a potential means of reducing hydrodynamic resistance. Although favorable wake and wave-interference patterns can reduce resistance in specific configurations, translating these benefits into deployable formation-control laws remains challenging. The central difficulty is cross-layer: hydrodynamic models must capture spacing- and speed-dependent interaction effects while remaining sufficiently compact, differentiable, and transferable for constrained real-time control. This review examines hydrodynamics-aware formation control for energy-efficient multi-vessel systems using a structured Scopus corpus of 193 publications. It connects three research layers that are commonly treated separately: formation-control architecture, hydrodynamic-interaction modeling, and energy-accounting fidelity. The literature is classified using a five-level evidence axis ranging from single-vessel energy optimization to closed-loop interaction-aware formation control. Empirical, potential-flow, CFD/RANS-based, reduced-order, and surrogate models are compared in terms of real-time capability, differentiability, calibration burden, transferability, and compatibility with MPC and distributed MPC. The synthesis shows that the literature contains substantial CFD, experimental, and offline configuration-optimization evidence, but very limited closed-loop validation in which hydrodynamic interaction, propulsion-realistic energy metrics, actuator constraints, communication limitations, and safety requirements are addressed together. Control-ready hydrodynamic surrogates, propulsion-aware energy accounting, robustness in mixed traffic, and field validation are therefore identified as the highest-priority research directions. A validation ladder and reporting checklist are proposed to improve comparability and support future deployment in inland, coastal, and open-water scenarios.

This paper introduces a model predictive control (MPC) strategy for solid oxide fuel cell (SOFC) systems, introducing thermal stress-aware power modulation. The proposed MPC approach incorporates a temperature rate-of-change constraint to manage local temporal and spatial temperature gradients in the SOFC during transient power modulation. The study evaluates the sensitivity and effectiveness of the temperature rate-of-change constraint under four different constraint parameter sets, spanning a range from fast to slow power modulation. A one-dimensional spatially discretised SOFC model is employed in the simulations to assess the resulting local temperature gradients. The results of this paper indicate that the proposed MPC strategy enhances transient power tracking performance compared to the conventional approach of using an electrical current rate-of-change constraint with 1%–17%, without a significant increase in the local temporal and spatial temperature gradients in the SOFC.

Collision avoidance in maritime navigation, particularly between autonomous and conventional vessels, involves iterative and dynamic processes. Traditional path planning models often neglect the behaviours of surrounding vessels, while path predictive models tend to ignore ship interaction features essential in collision scenarios. This study proposes a decision-making framework for collision avoidance, particularly focused on the interaction between autonomous and conventional vessels. The framework integrates a human-preference-aware navigational trajectory prediction model into the collision avoidance process, enhancing the decision-making capabilities in dynamic and interactive environments. We first model human-controlled ship navigational preferences using a Long Short-Term Memory (LSTM) autoencoder combined with K-means clustering, by extracting key preferences from ship pairs identified through AIS data. These preferences, which reflect strategic trajectory adjustments in response to collision risks, are then incorporated into trajectory prediction using a Multi-Task Learning Sequence-to-Sequence (seq2seq) attention LSTM model. The predicted trajectories provide a basis for the decision-making framework, including a local path planner and a trajectory tracking controller, designed to dynamically adjust the predicted reference path for collision-free navigation and ensure its accurate tracking. The framework was validated using AIS data from the port of Rotterdam, identifying four distinct navigational preferences by combining an LSTM-autoencoder and clustering techniques and demonstrating improved prediction accuracy compared to other existing models. Simulation tests demonstrate that the framework utilises the predicted trajectories to inform decision-making, ensuring accurate path tracking while dynamically addressing collision risks for autonomous ships. By providing preference-aware and adaptive reference trajectories, the framework reduces the likelihood of MASS trajectory misinterpretation by conventional ships, thereby supporting proactive collision avoidance in mixed waterborne transport environments.

Ammonia is considered one of the most promising hydrogen and energy carriers for decarbonizing deep-sea shipping and other remote heavy-duty applications. The AmmoniaDrive power plant concept uniquely combines Solid-Oxide Fuel Cell (SOFC) and Internal Combustion Engine (ICE) technology to address the issue of how to convert e-ammonia, produced from renewable resources, into useful on-board power safely and effectively, without the need for fossil fuels as combustion promotor. This paper introduces the AmmoniaDrive concept, outlines the challenging combustion properties of ammonia and ammonia-hydrogen mixtures and provides a short review of Compression Ignition ICE research for ammonia-fuelled engines. Three promising combustion concepts are introduced to give direction to further numerical and experimental research.

This work presents a stochastic model predictive control approach to optimize the management of a meat supply chain with uncertain demand. The proposed approach considers the temperature-dependent deterioration of meat products and the multi-stage nature of the supply chain, including producers, warehouses, retailers, and customers. The management problem is formulated as a mixed-integer optimization problem, where the objective is to minimize the total cost of the supply chain while satisflying customer demand and quality requirements. The approach uses scenario-based optimization to account for different uncertainty sources. The results show that the proposed method effectively balances the conflicting objectives of minimizing costs and meeting demand and quality requirements while accounting for uncertainty.

Discrete manufacturing companies are challenged to transform their existing manufacturing system to be better prepared for the changes caused by unstable supply chains and new market regulations. Reconfigurable Manufacturing Systems is a manufacturing paradigm conceived to deal with change in a fast and cost-effective way. Most of the design methods for such recon-figurable systems do not take the information of existing manufacturing systems into account. Thus, this paper proposes a method to generate reconfigurable manufacturing alternatives from legacy factories' information. The proposed method uses the bill of materials and the initial production capacity as inputs. The potential use of the proposed method is demonstrated with an illustrative example.

Maritime Autonomous Surface Ships (MASS) are advancing the shipping industry, requiring a mixed waterborne transport system (MWTS) where human supervision provides a supporting role for maintaining safety and efficiency, particularly in complex scenarios. This study explores the dynamics of seafarers’ trust in MASS during collision avoidance (CA) scenarios involving a vessel approaching from the starboard side. An empirical study with 26 participants representing diverse maritime experience levels examined how time, demographic factors, and collision avoidance strategies influence trust. Using a linear mixed model (LMM), trust was found to fluctuate across navigation stages: gradual accumulation during the routine navigation stage, sharp dissipation during strategy determination and execution stages, and partial recovery at the final stage. Strategies aligned with maritime regulations and appropriately timed evasive actions fostered higher trust, while overly early or imminent actions reduced trust. Additionally, a factor analysis consolidated the five trust dimensions, including dependability, predictability, anthropomorphism, faith, and safety, into two aspects: System Competence, encompassing the first four dimensions, and Situational Safety, representing safety-related trust. Furthermore, Bayesian Network (BN) is developed to model trust in the autonomous decision-making of MASS, integrating human observers demographics and situational factors. The model captures sequential trust dependencies, revealing the cascading effects of trust across various stages and the role of System Competence in shaping overall trust in the entire decision-making process. These findings provide actionable insights for designing MASS that support trust-building and optimise collision avoidance strategies, contributing to safer and more efficient autonomous maritime operations.

The inland waterway transport sector is facing increasingly stringent legislation to reduce emissions and improve energy efficiency. Speed planning has the potential to provide logistically compliant, energy-efficient, and emission-reducing voyages for inland vessels. However, current speed planning methods do not consider PM and NO_x emissions, nor do they consider alternative power systems to internal combustion engines (ICE) and full electric systems. These omissions have led to a lack of clarity on the impact of speed planning on the emission profile of inland vessels and the impact of alternative power systems on energy consumption. In this paper we propose a validated speed planning method that considers the emission profile (CO₂, PM₁₀, and NO_x) and different engine types for inland vessels in an leg-based speed planning approach while taking into account varying fairway water depth and speed. Through a use case we show that the vessel can achieve a 7.26% energy, 5.37% CO₂ and fuel, 3.85% NO_x, and 6.77% PM₁₀ reduction while maintaining the same arrival time; showing a distinct difference of this method compared to slow steaming. We also find that CO₂, NO_x, PM₁₀, and energy are not directly proportional when making speed adjustments. Finally, we analyze the adverse effects of emission control areas and emission limits on the energy consumption and arrival times of vessels with non-zero emissions propulsion.

The application of automated ground vehicles (AGVs) is well-established in closed environments such as port terminals, while their operation in open areas remains challenging. In this work, we set out to overcome this limitation by introducing platooning as a transfer mode in heterogeneous vehicle networks. We propose a collaborative transportation framework where different transportation companies use a shared platform for delivery tasks. To support decarbonization efforts in port hinterland transport, we consider fleets comprising electric AGVs (E-AGVs) and conventional trucks. These E-AGVs need to visit charging stations, modeled as battery swap stations (BSS), and join platoons to travel within the linking road segment. Each carrier has contracts with certain BSSs and shares these stations through the platform as part of the transportation plan. The platform functions as a demand and resource pooling mechanism, further offering platooning and infrastructure-sharing services. We model the interaction between the platform and carriers as a two-level constrained Stackelberg competition. An efficient solution algorithm, incorporating problem-specific heuristics and an adaptive large neighborhood search with dedicated destroy, repair, and intensification operators, is proposed. Extensive numerical experiments demonstrate the algorithm's performance on both existing and new benchmark instances. Our results highlight the platform's potential to streamline port-hinterland logistics, with E-AGV platoons significantly reducing costs and emissions.

As the maritime industry moves toward fully autonomous operations, it is becoming increasingly important to assess the control performance and safety of Maritime Autonomous Surface Ships. This chapter presents a structured framework designed to facilitate testing and data collection using autonomous ship systems, thereby supporting the verification that autonomous operations align with International Maritime Organization standards. We discuss the integration of key hardware and software components for robust autonomous operation and evaluate these systems using analytical performance criteria in both simulated and real-world scenarios. The chapter concludes by proposing new key performance indicators necessary for the continued development of autonomous maritime systems. Through extensive datasets and collaborative research via Open Science-focused algorithms and designs, we aim to set the groundwork for future advancements in this field.

This article focuses on the problem of collaborative collision avoidance (CCAS) for autonomous inland ships. Two solutions are provided to solve the problem in a distributed manner. We first present a distributed model predictive control (MPC) algorithm that allows ships to directly negotiate their intention to avoid collision in a synchronous communication framework. Moreover, we introduce a new approach to shape the ship’s behavior to follow the waterway traffic regulations. The conditional convergence toward a stationary solution of this algorithm is guaranteed by the theory of the alternating direction method of multipliers (ADMM). To overcome the problem of asynchronous communication between ships, we adopt a new asynchronous nonlinear ADMM (Async-NADMM) and present an asynchronous distributed MPC algorithm based on it. Several simulations and field experiments show that the proposed algorithms can guarantee a safe distance between ships in complex scenarios while following the traffic regulations. Furthermore, the asynchronous algorithm has an efficient computational time and satisfies the real-time computing requirements of ships in field experiments.