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This study proposes a novel integrated strategy aimed at enhancing both energy efficiency and safety levels in maritime operations by leveraging advanced ship trajectory optimization and motion control technologies. The paper quantitatively analyzes CO2 emissions and fuel consumption using the International Maritime Organization’s Energy Efficiency Operational Indicator (EEOI). Additionally, it employs Particle Swarm Optimization (PSO) algorithms to adjust and optimize ship routes, thereby significantly reducing energy consumption. Moreover, the research delves into precise ship motion control under constraints and uncertainties within a Multiple-Input–Multiple-Output (MIMO) nonlinear system environment. It achieves this by utilizing asymmetric barrier Lyapunov functions (ABLF) and adaptive neural networks (NN), which together ensure robust and reliable control performance. By integrating these advanced methodologies, the study provides comprehensive solutions that are applicable for sustainable and safe maritime operations. Simulation results demonstrate the effectiveness of using PSO to design vessel trajectory to reduce vessel fuel consumption and the effectiveness of ABLF and adaptive NN for ship safe motion control.
Liu et al. (Tue,) studied this question.
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