Neural network modeling and generalized predictive control for an autonomous underwater vehicle

This paper investigates the application of neural network based generalized predictive motion control to an autonomous underwater vehicle. The modified Elman neural network is used as the multi-step predictive model, the fused identification model is proposed to improve the predictive and control precision. The modified Elman neural network on-line learning improves the control system adaptability to the unpredicted operating environment for autonomous underwater vehicle. Simulations on autonomous underwater vehicle yaw velocity control are included to illustrate the effectiveness of the proposed control scheme.