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.

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.

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.

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 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.

The use of solid oxide fuel cells (SOFC) offers an alternative energy-conversion technology for the maritime sector, supporting the transition to renewable fuels. However, operating SOFCs for onboard power generation requires them to accommodate dynamic load changes, which introduces thermal stress, accelerates degradation, and reduces their operational lifetime. This work introduces a set of load-tracking model predictive control (MPC) strategies that reduce thermal stress by introducing spatial temperature gradient constraints (STGC), temporal temperature gradient constraints (TTGC) and temporal temperature gradient cost (TTGQ) components. The development of a spatially resolved one-dimensional prediction model for the SOFC stack is essential for incorporating these components into MPC strategies. The strategies are evaluated via simulations across multiple scenarios using key performance indicators (KPIs) for thermal stress, load-tracking performance and electrical efficiency, and benchmarked against a baseline MPC and a current-ramping-limit (CRL) approach. The results show that the STGC effectively reduces and constrains the spatial temperature gradient while maximising electrical efficiency. Furthermore, the TTGC and TTGQ strategies improve dynamic load-tracking response while resulting in lower temporal temperature gradients than a CRL.

Offshore wind farms face unique challenges in maintenance due to harsh weather, remote locations, and complex logistics. Traditional maintenance strategies often fail to optimize operations, leading to unplanned failures or unnecessary servicing. In recent years, deep reinforcement learning (DRL) has shown clear potential to tackle these challenges through a data-driven approach. This paper provides a critical review of representative DRL models for offshore wind farm maintenance planning, elaborating on both single- and multi-agent frameworks, diverse training algorithms, various problem formulations, and the integration of domain-specific knowledge. The review compares the benefits and limitations of these methods, identifying a significant gap in the widely adopted use of simplistic binary maintenance decisions, rather than including multi-level or imperfect repairs in the action space. In conclusion, this work suggests directions for future research to overcome current limitations and enhance the applicability of DRL methods in offshore wind maintenance.

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.

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.

Offshore Wind Farms (OWF) are expected to play a crucial role in mitigating climate change and promoting sustainable development, nevertheless, Operation and Maintenance (O&M) costs can reach 25–30 % of the total cost. Efficient O&M strategies reduce maintenance frequency, downtime, and improve overall performance. This paper reviews O&M decision-making for fixed-bottom OWFs according to a three-level decision-making hierarchy, strategic, tactical, and operational, reflecting how decisions vary by scope and time horizon of reference. Strategic decisions are typically focused on the overall maintenance strategy for the wind farm. Tactical decisions are focused on the selection of the fleet and the management of spare parts. Operational decisions are focused on the scheduling of individual maintenance tasks on a daily or weekly basis and the routing of the vessels. Exploring how the different decision-making layers have been addressed in the literature leads to valuable insights into open challenges and paves the way for the development of new decision-making methods. In this paper we highlight the untapped potential of prognostic-driven scheduling, we identify the lack of comprehensive models and methods that encompass all decision-making echelons holistically, and we emphasize the need for the integration of environmental considerations into the decision-making process.

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.

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.

Waterborne transport is very important for moving freight and passengers globally. To make this transport more efficient, vessel design must adapt to changing missions, regulations and the occurrence of malfunctions. This paper presents the design of an intelligent decision-support framework to assist marine engineers and vessel operators in updating the system and control architecture of marine vessels before and during a mission. The connection between the system architecture and control design perspectives is enabled using a semantics-based technique. To this end, the multi-level vessel control system is described by a semantic database, a knowledge graph used to connect the components automatically, and quantitative service criteria. Considering the system architecture, the optimal modification is deduced using modularity and complexity criteria, originating from the field of network theory. On the control side, an intelligent automation supervisor is designed to make offline and online decisions regarding the energy deficit to execute a new mission and the active automation configuration during operation. For offline decisions, system architecture modifications are requested by the vessel designers to cover the energy deficit. During operation, switching between hardware and virtual sensors as well as switching between energy management controllers is implemented to handle the effects of sensor faults. The framework is successfully applied to a case study of a tugboat used to adapt to missions with different power requirements, while simulation results are used to indicate its application in supporting the decisions of vessel designers and human vessel operators.

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.

Autonomous inland shipping offers a safer and more efficient form of transportation over water with the potential to reduce maritime carbon emissions. However, the operation of autonomous vessels presents unique challenges due to complex dynamics, varying traffic conditions, and environmental disturbances. To ensure the safe navigation of these vessels in confined inland waterways, it is crucial to address manoeuvring prediction and motion control challenges. Research focusing on these challenges disregards or only partially incorporates inland waterway characteristics related to the vessel and its surroundings. This study provides a comprehensive analysis of these key factors. By modelling the vessel using a modified Manoeuvring Modelling Group (MMG) model specifically tailored for confined waterways, hydrodynamic effects due to shallow water, channel banks, and current are accounted for. A nonlinear model predictive controller (NMPC) is employed for the vessel path following control under various scenarios, including straight channels, confluences, and river bends. It is observed that the hydrodynamic effects from the channel banks significantly impact vessel steering. Compared to conventional proportional-integral-derivative (PID) controllers, NMPC effectively reduces course deviations and cross-track errors under varying water depth and ship-to-bank distance conditions, while also requiring fewer rudder deflections. Furthermore, key performance metrics related to the control of inland waterway vessels are proposed to evaluate the controller's performance further. The NMPC control law demonstrates its effectiveness in capturing the hydrodynamic effects and improving navigation safety in confined waterways.

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.

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.

Solid Oxide Fuel Cells are promising power generation technologies, especially for large-scale applications. As the marine industry is targeting a full de-carbonization by year 2050, increasing attention is being directed toward the implementation of these technologies. Solid Oxide Fuel Cells are complex systems where thermodynamics and electrochemical reactions are coupled, resulting in highly non-linear dynamics, tight operational constraints, and multiple distributed sensors. Those quantities that cannot be directly measured, need to be estimated. Among these, the so called Area Specific Resistance is an indicator of cell's health condition, related to the cell degradation. This paper proposes a Moving Horizon Estimator based on an extended state-space model of a methane-fueled Solid Oxide Fuel Cell, to estimate in real time the Area Specific Resistance of the cell. Using the estimated value, along with its maximum and average rates, a predictive framework is developed to estimate the Remaining Useful Life of the cell. Simulations are used to illustrate the application and the efficiency of the proposed method.

This paper considers the presence of movable bridges in inland waterway transport, and presents a control framework for the joint dynamic coordination of bridge operations and autonomous vessel navigation to minimize waiting times of vessels at bridges. Simultaneous evolution of bridge occupancy and vessel position is captured by a control-oriented model that incorporates qualitative behavior in the form of propositional logic expressions. A model predictive control (MPC) strategy is designed considering adaptable bridge opening regimes to exploit vessel passage demand, and operational preferences of both vessel skippers and bridge operators are taken into account to reach fair trade-off decisions. A realistic case study pertaining to the Rhine-Alpine corridor is used to demonstrate the effectiveness of the approach. Appropriate key performance indicators (KPIs) are defined and employed for a quantitative comparison with a mixed-integer programming (MIP) strategy with fixed opening regimes. Furthermore, sensitivity to the main MPC parameters is examined by carrying out extensive testing to assess the effect of each design parameter on the solution.

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.