Model updating and thruster fault diagnosis for underwater vehicle

As the impact of underwater vehicle dynamics modeling error on fault diagnosis system, a method using improved Elman neural network to modify underwater vehicle dynamics model in the current is proposed. The neural network parameter adjustment law under the Lyapunov stability is given. Sliding mode observer is constructed for state estimation based on the modified dynamics model. The change of state estimation residual of each DOF is analyzed when fault occurs in different thrusters of under vehicle. A thruster fault diagnosis method based on residual state fusion is presented, which has been validated through AUV sea trials data.