Power availability to preserve propulsion is a vital issue in the shipping industry which relies on persistent power generation and maintaining the stability of the power and propulsion system. Since the introduction of on-board all-electric Direct Current Power and Propulsion Systems (DC-PPS) with hybrid power generation, which are more efficient compared to direct-diesel and Alternating Current (AC) all-electric configurations, there have been extensive investigations on stabilization and power generation control to enable robust and reliable performance of DC-PPS during different ship operations. In this paper, a multi-level approach is proposed for hybrid power generation control. For this goal, first, a mathematical model is proposed for each power system component and then, the overall on-board power system is modeled in a state space format. Then, a multi-level Model Predictive Control (MPC) approach is proposed for the DC voltage control which unlike conventional droop control approaches, takes the DC current generated by power sources into account explicitly. The performance of the proposed approach is evaluated via several simulation experiments with a high fidelity model of a high voltage DC-PPS. The results of this paper lead to enabling more effective approaches for power generation and stability control of constant power loaded microgrids.

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The maritime industry is actively exploring alternative fuels and drive train technology to reduce the emissions of hazardous air pollutants and greenhouse gases. High temperature solid oxide fuel cells (SOFCs) represent a promising technology to generate electric power on ships from a variety of renewable fuels with high efficiencies and no hazardous emissions. However, application in ships is still impeded by a number of challenges, such as low power density and high capital cost. A slow response to load transients is another challenges, which is typically a result of the conservative thermal management strategies used to ensure that excessive thermal stresses in the stack are avoided. Therefore, a reduced order SOFC stack model is developed in this work for model-based control. The model is subsequently verified with a high fidelity model developed in previous work. In addition, a preliminary framework for its use for model predictive SOFC control is provided. The reduced order model and control framework will be used in future work to optimise thermal management of SOFC stacks for improved transient response while respecting physical and operational constraints.@en

Pontryagin's Minimum Principle is a way of solving hybrid powertrain optimal energy management. This paper presents an improvement of a classical implementation. The core of this improvement consists in relaxing the tolerance on some intermediate steps of the algorithm in order to reduce the number of iterations and thereby reducing the number of operations required to compute an optimal solution. The paper describes both a classical implementation of Pontryagin's Minimum Principle as well as the improved version. Numerical simulations are conducted on an academic example to demonstrate the benefits of the proposed approach.

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The shipping industry is facing increasing demands to reduce its environmental footprints. This has resulted in adoption of new and more environmental friendly power sources and fuels for on-board power generation. One of these novel power sources is the Solid Oxide Fuel Cell (SOFC) which has a great potential to act as a power source, thanks to its high efficiency and capability to handle a wide variety of fuel types. However, SOFCs suffer from low transient capabilities and therefore have never been considered to be used as the main power source for maritime applications. In this paper, novel component sizing, energy and power management approaches are proposed to enable the use of SOFCs as the main on-board power source for the first time in the literature and integrate them into the liquefied natural gas fueled Power and Propulsion System (PPS) of vessels. The proposed component sizing approach determines the power ratings of the on-board sources (SOFC, gas engine and battery) considering size and weight limits, while the energy and power management approaches guarantee an optimal power split between different power sources and PPS stability while looking after battery aging. The results indicate that the combined proposed optimization-based approaches can yield up to 53% CO₂ reduction and 21% higher fuel utilization efficiency compared to conventional diesel-electric vessels.

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Formation control of autonomous surface vessels (ASVs) has been studied extensively over the last few years since it offers promising advantages. In this paper, two control methods for distributed leader-follower formation control are proposed: A Nonlinear Model Predictive Control (MPC) method and an MPC method using Feedback Linearization. One agent per vessel performs planning and control. The agents exchange information on their current and predicted positions. The two proposed methods are compared with each other and also with a conventional Proportional-Integral (PI) control method. The performance of the proposed strategies is evaluated through simulations and field experiments using small scale vessels. The simulation and field experiment results show that the proposed MPC-based approaches outperform the conventional PI control method.@en

Pontryagin’s Minimum Principle is a way of solving hybrid powertrain optimal energy management. This paper presents an improvement of a classical implementation. The core of this improvement consists in relaxing the tolerance on some intermediate steps of the algorithm in order to reduce the number of iterations and thereby reducing the number of operations required to compute an optimal solution. The paper describes both a classical implementation of Pontryagin’s Minimum Principle as well as the improved version. Numerical simulations are conducted on an academic example to demonstrate the benefits of the proposed approach.

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Ship hybridization has received some interests recently in order to achieve the emission target by 2050. However, designing and optimizing a hybrid propulsion system is a complicated problem. Sizing components and optimizing energy management control are coupled with each other. This paper applies a nested double-layer optimization architecture to optimize the sizing and energy management of a hybrid offshore support vessel. Three different power sources, namely diesel engines, batteries and fuel cells, are considered which increases the complexity of the optimization problem. The optimal sizing of the components and their corresponding energy management strategies are illustrated. The effects of the operational profiles and the emission reduction targets on the hybridization design are studied for this particular type of vessel. The results prove that a small emission reduction target of about 10% can be achieved by improving the diesel engine efficiency using the batteries only while the achievement of a larger emission reduction target mainly depends on the amount of the hydrogen and/or on-shore charging electricity consumed. Some design guidelines for hybridization are derived for this particular ship which could be also valid for other vessels with similar operational profiles.

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The future autonomous ships will be operating in an environment where different autonomous and non-autonomous vessels with different characteristics exist. These vessels are owned by different parties and each uses its owned unique approaches for guidance and navigation. The Collaborative Autonomous Shipping Experiment (CASE) aims at emulating such an environment and also stimulating the move of automatic ship control algorithms towards practice by bringing together different institutes researching on autonomous vessels under an umbrella to experiment with collective sailing in inland waterways. In this paper, the experiments of CASE 2020 are explained, the characteristics of different participating vessels are discussed and some of the control and perception algorithms that are planned to be used at CASE 2020 are presented. CASE 2020 will be held in parallel to iSCSS 2020 at Delft University of Technology, the Netherlands.

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The maneuvering control of autonomous vessels has been under extensive investigations by academic and industrial communities since it is one of the primary steps towards enabling unmanned shipping. In this paper, a model predictive control (MPC) approach is presented for trajectory tracking control of vessels which takes into account the thrust allocation (TA) problem in the presence of rotatable thrusters. In this approach, the TA problem is formulated over a finite horizon and solved with regard to the power consumption, changes in the angle and speed of actuators, and the operating constraints. In the proposed control approach, several linearization techniques have been employed to enable the adoption of quadratic programming approaches for solving the MPC's and TA's optimization problems. The performance of the proposed approach is evaluated through several simulation experiments using a replica vessel model.

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In this paper, a distributed control approach is proposed to enable fuel-efficient Vessel Train Formations (VTF) in inland waterways and port areas for addressing the efficiency and environmental issues of transport over water. For path tracking, collision avoidance, and consensus over the VTF speed a distributed Model Predictive Control (MPC) algorithm is adopted which uses the Alternating Direction Method of Multipliers (ADMM) to guarantee path following and consensus between vessels. The all-electric Direct Current (DC) configuration is considered for the Power and Propulsion Systems (PPS) of the autonomous vessels under study. Considering their PPS specification, the vessels negotiate with each other to agree on the most efficient speed for all the vessels in the VTF. Simulation results suggest that a significant amount of fuel saving can be obtained by using the proposed approach.

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With the advent of on-board Direct Current (DC) power and propulsion systems, the transmission and delivery of energy on board of ships can be carried out more efficiently as it is being done using conventional direct-diesel or Alternative Current (AC) power and propulsion systems. However, the stability of DC voltage on-board of all-electric ships with a DC power and propulsion architecture is a critical issue that has drawn attention over the last few years. In this paper, a novel Model Predictive Control (MPC) approach is proposed for the diesel-generator shaft speed control and DC voltage regulation on-board of all-electric ships, focusing on the uncontrolled rectification at the voltage conversion stage. This work considers the prime mover as a Diesel–Generator–Rectifier (DGR) set which feeds propulsive asynchronous motors through a DC-link. First, a state space model dynamic model is developed for the DGR set and the DC-link. Then, the MPC-based approach is presented. The approach is based on Input–Output Feedback Linearization (IOFL) which is used for the linearization of the highly non-linear dynamics of the system. To increase the robustness of the algorithm, a tube-based technique is adopted which is implemented through a linear auxiliary control law. Different analyses are carried out to show that the proposed control strategy is capable of handling sudden changes in load conditions as well as adverse effects of Constant Power Loads (CPL).@en

Over the last few years, autonomous shipping has been under extensive investigation by the scientific community where the main focus has been on ship maneuvering control and not on the optimal use of energy sources. In this paper, the purpose is to bridge the gap between maneuvering control, energy management, and the control of the Power and Propulsion System (PPS)to improve fuel efficiency and the performance of the vessel. Maneuvering control, energy management, and the control of the PPS are in the literature typically studied independently from one another, while they are closely connected. A generic control methodology based on receding horizon control techniques is proposed for the ship maneuvering control as well as energy management. In the context of this research, Direct Current (DC)all-electric architectures are considered for the PPS where the relationship between the produced power by energy sources and vessel propellers is established by a DC microgrid. The objective of the proposed approach is to ensure the ship mission objectives by guaranteeing efficient power availability, decreasing the trajectory tracking error, and increasing the fuel efficiency. In this regard, for the ship motion control, a Model Predictive Control (MPC)algorithm is proposed which is based on Input–Output Feedback Linearization (IOFL). Through this algorithm, the required power for the ship mission is predicted and then, transferred to the proposed Predictive Energy Management (PEM)algorithm which decides on the optimal split between different on-board energy sources during the mission. As a result, the fuel efficiency and the power system stability can be increased. Several simulations are carried out for the evaluation of the proposed approach. The results suggest that by adopting the proposed approach, the trajectory tracking error decreases and the Specific Fuel Consumption (SFC)efficiency is significantly improved.

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In the last few years, autonomous shipping has been under extensive consideration by academic and industrial communities as well as governmental organizations due to several potential advantages that it introduces. Furthermore, due to the drastic environmental consequences of transport overwater, international organizations have enforced the shipping industry to reduce its emissions significantly. As a result, the emergence of sustainable autonomous shipping seems inevitable...@en

Motion control is one of the most critical aspects in the design of autonomous ships. During maneuvering, the dynamics of propellers as well as the craft hydrodynamical specifications experience severe uncertainties. In this paper, an adaptive control approach is proposed to control the motion and trajectory tracking of an autonomous vessel by adopting neural networks that is used for estimating the dynamics of the propellers and handling hydrodynamical uncertainties. Considering that the maneuvering model of a vessel resemble a nonlinear non-affine-in-control system, the proposed neural-based adaptive control algorithm is designed to estimate the nonlinear influence of the input function which in this case is the dynamics of propellers and thrusters. It is also shown that the proposed methodology is capable of handling state dependent uncertainties within the ship maneuvering model. A Lyapunov-based technique and Uniform Ultimate Boundedness are used to prove the correctness of the algorithm. To assess the method's performance, several experiments are considered including trajectory tracking simulations in the port of Rotterdam.

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In this paper, a control strategy is proposed for the voltage regulation and the shaft speed control of diesel generators on-board of all-electric ships with Direct Current (DC) power and propulsion systems. The proposed methodology is based on Input-Output Feedback Linearization (IOFL) of the prime mover dynamical model. First, a model for different components in the system is represented and by merging them, the overall model of the system is obtained in state space format. Then, an IOFL-based control algorithm is applied for stabilization, voltage regulation and shaft speed control of the diesel generator. The performance of the algorithm is assessed using a model of an inland vessel.@en

In this paper, a predictive power management algorithm is proposed for all-electric ships with DC power distribution architecture with which it is insured that the provided power by each set of the Diesel-Generator-Rectifier (DGR) settles around the optimal point on Specific Fuel Consumption (SFC) curve of the diesel engine. To increase the stability of the on-board power system, using this algorithm, it is also guaranteed that the provided power by DGRs does not undergo tremendous changes over short time intervals. Prior to the algorithm introduction, the paper deals with the modeling of the DC power and propulsion system as well as one dimensional ship hydrodynamics model. Furthermore, a Model Predictive Control (MPC) algorithm is proposed for the purpose of ship surge motion control where the demanded power over a bounded horizon is computed to be used later by the power management algorithm.

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In this paper, a neural network-based adaptive control algorithm is proposed for a class of non-affine systems where the nonlinear influence of the system input on the states is unknown. The algorithm transforms the problem of controlling non-affine systems to control of nonlinear affine systems and then, by approximating the inverse of the input function, calculates feasible control input. Lyapunov technique, Uniform Ultimate Boundedness and Matrix Singular Values are used for stability analysis and design of the controller. In order to investigate the performance of the algorithm, it is applied to an autonomous vessel where the dynamics of the propeller is unknown.

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In this paper, the convergence rate and time analysis of a fault-tolerant consensus algorithm that we proposed in [1] is carried out for asynchronous and synchronous partially connected networks with delay on communication paths. The results are also extended to the case of networks with time-varying underlying graph topology.

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