Radial Basis Function Neural Network Sliding Mode Control for Ship Path Following Based on Position Prediction

Abstract

In the process of ship navigation, due to the characteristics of large inertia and large time delay, overshoot can easily occur in the process of path following. Once the ship deviates from the waypoint, it is prone to grounding and collision. Considering this problem, a sliding mode control algorithm based on position prediction using the radial basis function (RBF) neural network is proposed. The desired heading angle is designed according to a backstepping algorithm. The hyperbolic tangent function is used to design the sliding surface, and the course is controlled by sliding mode control. The second-order Taylor expansion is used to predict the future position, the current error and future error functions are constructed, and the total errors are fed back to the desired heading angle. In the sliding mode control system, the RBF neural network is used to approximate the total unknown term, and a velocity observer is introduced to obtain the surge velocity and sway velocity. To verify the effectiveness of the algorithm, the mathematical model group (MMG) model is used for simulation. The simulation results show the effectiveness and superiority of the designed controller. Therefore, the RBF neural network sliding mode controller based on predicted position has robustness for ship path following.