In this paper, an adaptive neural network control method based on state observers is proposed for the rolling motion control problem of zero-speed ships. By combining the neural network with the disturbance observer technique, the internal and external uncertainties of the rolling motion control system of zero-speed ships are overcome. Considering that the ship's rolling angular velocity is difficult to be measured accurately under the actual sea state, the state observer is introduced to estimate the unknown rolling angular velocity. Finally, the simulation results verify the effectiveness of the control scheme.
In this thesis, the path planning for the pirate attack incidents encountered during the navigation of the ship is firstly carried out. On this basis, the path planning of other dynamic obstacles in the pirate attack area is further considered. On this basis, the path planning of other dynamic obstacles in the pirate attack area is further considered. The grid method is used to model the research sea area, and the dual-objective model is constructed to ensure the minimum fuel cost and risk cost at the same time. The grid method is used to model the research sea area, and the dual-objective particle swarm algorithm to solve the problem. calculation results based on the optimal solution of pareto are better than the results obtained by the weight method, and the results are simulated on a It is concluded that the calculation results based on the optimal solution of pareto are better than the results obtained by the weight method, and the results are simulated on a large-scale navigation simulator, which proves the validity and feasibility of the model. The results are simulated on a large-scale navigation simulator, which proves the validity and feasibility of the model.
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