State observer is required to obtain unknown yaw rate. A fuzzy logic system (FLS) is adopted to approximate the unknown uncertainties caused by the changes in water depth, wind, wave, ship loading, and speed in navigation. This paper develops an autopilot heading control scheme based on a fuzzy state observer for an intelligent ship on this subject to track the prescribed function while calling for performance limitation and order execution time. Cost efficiency and high performance demand advanced technologies for the ship control systems. Traffic engineering control is a major challenge in marine transportation. Therefore, the RBF neural network sliding mode controller based on predicted position has robustness for ship path following.
The simulation results show the effectiveness and superiority of the designed controller.
To verify the effectiveness of the algorithm, the mathematical model group (MMG) model is used for simulation. 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. 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. The hyperbolic tangent function is used to design the sliding surface, and the course is controlled by sliding mode control. The desired heading angle is designed according to a backstepping algorithm. Considering this problem, a sliding mode control algorithm based on position prediction using the radial basis function (RBF) neural network is proposed. Once the ship deviates from the waypoint, it is prone to grounding and collision. 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.
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