Adaptive neural network-based backstepping fault tolerant control for underwater vehicles with thruster fault

Abstract

A thruster fault tolerant control (FTC) method is developed for underwater vehicles in the presence of modelling uncertainty, external disturbance and unknown thruster fault. The developed method incorporates the sliding mode algorithm and backstepping scheme to improve its robustness to modelling uncertainty and external disturbance. In order to be independent of the fault detection and diagnosis (FDD) unit, thruster fault is treated as a part of the general uncertainty along with the modelling uncertainty and external disturbance, and radial basis function neural network (RBFNN) is adopted to approximate the general uncertainty. According to the Lyapunov theory, control law and adaptive law of RBFNN are derived to ensure the tracking errors asymptotically converge to zero. Trajectory tracking simulations of underwater vehicle subject to modelling uncertainty, ocean currents, tether force and thruster faults are carried out to demonstrate the effectiveness and feasibility of the proposed method.