Design of robust adaptive course controller for unmanned surface vehicle with input saturation

On the basis of considering the saturation of the actuator, a robust adaptive course controller of unmanned surface vehicle is presented by introducing an auxiliary function. During the voyage, due to the change of working conditions and the interference of the external environment, the mathematical model of unmanned surface vehicle presents nonlinear characteristics and uncertainty. In view of the model nonlinearity and uncertainty, the RBF neural network is used to approximate the unknown function, and then based on the backstepping method and Lyapunov stability theory, a robust adaptive course controller with input saturation is proposed by means of a saturation aided design system. Finally, the results of numerical simulation show that the proposed control strategy can effectively track the target value and has strong robustness. © 2017 ICIC International.