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

Abstract

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.