Adaptive output feedback control based on DRFNN for AUV

The tracking control problem of AUV in six degrees-of-freedom (DOF) is addressed in this paper. In general, the velocities of the vehicles are very difficult to be accurately measured, which causes full state feedback scheme to be not feasible. Hence, an adaptive output feedback controller based on dynamic recurrent fuzzy neural network (DRFNN) is proposed, in which the location information is only needed for controller design. The DRFNN is used to online estimate the dynamic uncertain nonlinear mapping. Compared to the conventional neural network, DRFNN can clearly improve the tracking performance of AUV due to its less inputs and stronger memory features. The restricting condition for the estimation of the external disturbances and network's approximation errors, which is often given in the existing literatures, is broken in this paper. The stability analysis is given by Lyapunov theorem. Simulations illustrate the effectiveness of the proposed control scheme.