Adaptive neural network trajectory tracking control for an underactuated unmanned surface vehicle

Abstract

In this paper, we investigate an adaptive output feedback trajectory tracking controller for an underactuated unmanned surface vehicle (USV) with position signals. Using Lyapunov stability theorem and backstepping strategy, a Neural Network (NN)-based adaptive trajectory tracking control scheme is proposed without the velocity informations. The NN is employed to approximate the unknown external disturbances and model parametric uncertainties. Also, a high-gain observer is applied to estimate the USV’ unmeasured velocities, and the NN-based output feedback tracking control is designed. Simulation results are carried out to verify the proposed controller.