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Global marine technology trends 2030



Global Marine Technology Trends 2030 is the culmination of a collaborative project between Lloyd’s Register, QinetiQ and the University of Southampton. The report, released on Monday 7th September 2015, examines the transformative impact of eighteen technologies on ship design, on naval power and on the use of ocean space in 2030.
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This paper investigates the event-triggered adaptive neural trajectory tracking control of marine surface vessels (MSVs) under internal/external uncertainties and deception attacks. A novel adaptive neural trajectory tracking control solution is proposed. Under the backstepping framework, this work treats internal and external uncertainties and deception attacks as compound uncertainties, using neural network (NN) technologies and single-parameter learning ideas to reconstruct. In addition, to reduce the mechanical wear of the actuator, the event-triggered adaptive neural tracking control is developed.
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Technological innovation constantly transforms and redefines the human element’s position inside complex socio-technical systems. Autonomous operations are in various phases of development and practical deployment across several transport domains, with marine operations still in their infancy. This article discusses current trends in developing autonomous vessels and some of the most recent initiatives worldwide. It also investigates the individual and combined effects of maritime autonomous surface ships (MASS) on regulations, technology, and sectors in reaction to the new marine paradigm change. Other essential topics, such as safety, security, jobs, training, and legal and ethical difficulties, are also considered to develop a solution for efficient, dependable, safe, and sustainable shipping in the near future. Finally, it is advised that holistic approaches to building the technology and regulatory framework be used and that communication and cooperation among various stakeholders based on mutual understanding are essential for the MASS to arrive in the maritime industry successfully.
Conference Paper
With the advancements in the maritime industry, which delivers almost 90 percent of the world trade, the frequency of maritime activities has drastically increased resulting a major concern in maritime safety. A significant 30 percent of maritime accidents are caused due to bad weather conditions, for instance sea storms and strong winds created due to high turbulence and waves. The deaths and casualties caused due to these accidents would have been minimized if there was a mechanism for efficient emergency response. Autonomous Surface Vessels (ASVs) have been used for several disaster mitigation and recovery operations in hurricanes, earthquakes and tsunami. ASVs are comparatively cheap and safe to be deployed on to hazardous zones in the deep sea due to their long term marine presence. A more efficient way for emergency response by the ASVs would be, the ability to predict a location where there is a possibility for an accident to take place and position itself such that it could effectively respond to the emergency. Hence the author is proposing an optimal solution using machine learning techniques to suggest waypoints to ASVs for effective emergency response on human operated surface vessels.
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This paper investigates the tracking control problem of marine surface vessels (MSVs) in the presence of uncertain dynamics and external disturbances. The facts that actuators are subject to undesirable faults and input saturation are taken into account. Benefiting from the smoothness of the Gaussian error function, a novel saturation function is introduced to replace each nonsmooth actuator saturation nonlinearity. Applying the hand position approach, the original motion dynamics of underactuated MSVs are transformed into a standard integral cascade form so that the vector design method can be used to solve the control problem for underactuated MSVs. By combining the neural network technique and virtual parameter learning algorithm with the vector design method, and introducing an event triggering mechanism, a novel event-triggered indirect neuroadaptive fault-tolerant control scheme is proposed, which has several notable characteristics compared with most existing strategies: 1) it is not only robust and adaptive to uncertain dynamics and external disturbances but is also tolerant to undesirable actuator faults and saturation; 2) it reduces the acting frequency of actuators, thereby decreasing the mechanical wear of the MSV actuators, via the event-triggered control (ETC) technique; 3) it guarantees stable tracking without the a priori knowledge of the dynamics of the MSVs, external disturbances or actuator faults; and 4) it only involves two parameter adaptations—a virtual parameter and a lower bound on the uncertain gains of the actuators—and is thus more affordable to implement. On the basis of the Lyapunov theorem, it is verified that all signals in the tracking control system of the underactuated MSVs are bounded. Finally, the effectiveness of the proposed control scheme is demonstrated by simulations and comparative results.
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