融合CNN洋流预测的长航程AUV节能控制方法OA

For the energy-saving control problem of long-range autonomous underwater vehicles(AUVs)in ocean-current environments,a hierarchical model predictive control(MPC)method integrating ocean-current prediction by a convolutional neural network(CNN)was proposed.First,a CNN-based ocean-current predictor was established,by which the ocean-current field in the large-scale spatiotemporal region involved in AUV operations was forecasted.Then,an energy-consumption objective function was constructed based on the predicted ocean-current state variables,and the energy-optimal dynamic desired speed and heading angle were obtained through online solution.Finally,an MPC controller satisfying the AUV dynamics and boundary constraints was designed,in which the quadratic objective function was minimized with respect to the AUV desired-state deviations and the control inputs,and a further reduction in navigation energy consumption was achieved.Theoretical analysis and simulation results show that good robustness is possessed by the proposed scheme,and the total navigation energy consumption is reduced by 12.2％compared with the comparative method.