Predicting underwater unmanned vehicle dynamic recovery process in nonlinear watershed based on BP neural network

被引：0
|
作者：
Du X. [1 ]
Li H. [1 ]
Liu X. [1 ]
机构：
[1] School of Marine Science and Technology, Northwestern Polytechnical University, Xi′an
来源：
Xibei Gongye Daxue Xuebao/Journal of Northwestern Polytechnical University | 2024年 / 42卷 / 02期
关键词：
hydrodynamic coefficient; neural network; nonlinear watershed; underwater unmanned vehicle dynamic recovery;
D O I：
10.1051/jnwpu/20244220189
中图分类号：
学科分类号：
摘要：
Because of a nonlinear watershed′s interference during the recovery of an unmanned underwater vehicle (UUV), a closed-loop control method for optimizing the recovery path of the UUV based on the BP neural network is proposed. The paper uses the computational fluid dynamics(CFD) method to simulate the hydrodynamic coefficients for recovering the UUV relative to a submarine in different paths. The numerical simulation results are used as the initial data for training the BP neural network. Using the Latin super-law, the location of the nonlinear watershed is randomly sampled. Hydrodynamic coefficients of the UUV in the nonlinear watershed at sampling points are predicted based on the BP neural network. The results show that the error predicted by the neural network through root mean squares is within 10%. Through combining the prediction results of the neural network with the UUV longitudinal maneuverability equation, the error of the recovery speed and steering interval is compared with the theoretical recovery path. The closed-loop control method of UUV dynamic recovery in the nonlinear watershed is optimized. ©2024 Journal of Northwestern Polytechnical University.
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页码：189 / 196
页数：7
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