Adaptive neural network control system of bottom following for an underactuated AUV

The bottom following control problem of an underactuated autonomous underwater vehicle (AUV) was addressed in this paper. In order to deal with the parameter variations and uncertainties due to time-varying hydrodynamic damps, the RBF neural network (NN) was introduced to estimate unknown terms where an adaptive law was chosen to guarantee optimal estimation of the weight of NN. Based on the Lyapunov stability theorem, an adaptive NN controller was designed to guarantee all the error states in the diving control system were asymptotically stable. Two bottom profiles, one with constant slopes and the other with real measured data were used to evaluate the performance of the bottom following controller. Simulation results demonstrated that the proposed controller was effective to eliminate the disturbances caused by vehicle's nonlinear and model uncertainty.