Abstract
ABSTRACT This paper presents a novel gliding strategy for underwater gliders, which can better balance the glider energy consumption, motion accuracy and voyage velocity. This strategy is designed according to the performance analysis results of the glider under the conventional gliding strategy. In the shallow water areas with the higher current intensity, the glider adjusts the net buoyancy to a larger value, which can ensure the higher voyage velocity and motion accuracy. In the deep water areas with the smaller current intensity, the glider adjusts the net buoyancy to a smaller value, which can ensure the higher energy utilisation rate. In the different water areas, the movable mass block translation amount will also be adjusted. To improve the performance analysis efficiency, the radial basis function (RBF) neural network is employed to establish surrogate models of the glider dynamic model. Then, we carry out the glider control parameter optimisation using the surrogate models and non-dominated sorting genetic algorithm II. The comparison research between the conventional gliding strategy and the novel gliding strategy shows that the novel gliding strategy can make the glider have the higher motion accuracy under the specific energy utilisation rate. Besides, the novel gliding strategy will not affect the optimal solution of the glider voyage velocity and energy utilisation rate, and it improves the glider motion accuracy by mainly reducing the voyage velocity. The advantage of the novel strategy is mainly to improve the glider motion accuracy, which may provide certain guidance for the actual application of underwater gliders.
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