Fault diagnosis of underwater vehicle with FNN

Aiming at the character that the uncertainties of the complex system of underwater vehicle (UV) bring to model the system very difficult, a fuzzy neural network (FNN) with least adjustment is proposed to construct the motion model of UV. The adjustment of the dynamic learning rate and weights of FNN is studied. The FNN has the ability not only to approach the whole figure of a function but also to catch detail changes of the function, which makes the approaching effect preferably. Residuals are achieved by comparing the output of FNN with the sensor output. Fault detection rules are distilled from the residuals to execute thruster fault diagnosis. The feasibility of the method presented is validated by simulation experiment results.