Neural networks modelling and generalised predictive control for an autonomous underwater vehicle

This paper investigates the application of neural networks-based generalised predictive motion control for an autonomous underwater vehicle (AUV). The modified Elman neural networks (MENNs) are used as the multi-step predictive model, and the fused identification model is proposed to improve the predictive and control precision. The MENNs online learning improves the control system adaptability to the unpredictable operating environment for AUV. Simulations on AUV yaw velocity control are concluded to illustrate the effectiveness of the proposed control scheme.