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In this paper, a novel fault-tolerant algorithm for achieving approximate Byzantine consensus in asynchronous networks is proposed. It is shown that the topological condition required for the success of the algorithm is more relaxed than the previous results. We prove that the synchronicity of the network does not affect this condition and the algorithm succeeds in synchronous networks as well. The same fact is concluded in networks with delay on communication paths. Finally, we extend the results to networks with time-varying underlying graph.

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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.INDEX TERMS Hybrid, offshore support vessel, sizing, control, energy management.

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 sever 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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