Adaptive output feedback tracking control of surface vessels under input saturation

Abstract

In the presence of unmeasured velocity, unknown dynamic model parameters, unknown time-variant disturbances and input saturation, this paper develops an adaptive output feedback tracking control law for surface vessels using high-gain observer, radial basis function (RBF) neural networks, auxiliary dynamic system and the dynamic surface control (DSC). The high-gain observer is constructed to provide the estimates of unmeasured velocity vector, the RBF neural networks are employed to approximate the uncertainties of ship dynamics, the adaptive laws are designed to estimate the weights of RBF neural networks and the bounds of unknown time-variant disturbances, respectively, and the auxiliary dynamic system is used to deal with the input saturation. The dynamic surface control makes the designed control law be simple and easy to implement. It is proved that the designed tracking control law forces the ship to track the reference trajectory, while guaranteeing the uniform ultimate boundedness of all signals in the closed-loop trajectory tracking control system of ships. The simulation results illustrate the effectiveness of the proposed control law.