Surface Vehicle Path-Following Control Considering Uncertainties

This paper analyzes the path-following problem of unmanned vehicles when it is disturbed by uncertainties. Firstly, based on the kinematics and dynamics of unmanned vehicles, a path-following model is established and analyzed under disturbances. Secondly, a path-following controller is proposed, including two parts: output feedback linearization and neural network adaptive approximate compensation. In the first part, the concise mapping relationship between input and output is obtained by canceling the nonlinear term of the system, and then the feedback control law is designed. In the approximate compensation part of the neural network, a concise and effective adaptive law is designed to update the weights by using online learning technology of error filtering. Finally, theoretical analysis shows that all signals are uniformly ultimately bounded, and comparative simulation results verify the effectiveness and superiority of the proposed method.