Underactuated AUV Trajectory Tracking Sliding Mode Control with Input Limitation

Aiming at the horizontal plane trajectory tracking problem of the underactuated autonomous undersea vehicle (AUV) under external interferences and limited inputs, a sliding mode controller based on a nonlinear disturbance observer and radial basis function(RBF) neural network was proposed in this study. Firstly, the underactuated AUV kinematics model was transformed into an error kinematics model to stabilize the position error through a coordinate transformation. Secondly, the backward step method was used to design the bow-rocking angle virtual velocity control law to stabilize the attitude error. Subsequently, a nonlinear disturbance observer was used to estimate the disturbance of a time-varying ocean current, and the derivative of the virtual control law was estimated through a filter to avoid the “differential explosion” caused by the derivative of the virtual control law. Finally, an adaptive RBF neural network was designed to compensate the actual input of the underactuated AUV, and the Lyapunov stability proved that the signal used for the closed-loop tracking error was uniformly bounded. The simulation verified the effectiveness of the designed controller