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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.