PID Control Based on RBF Neural Network for Ship Steering

A PID control combined with Radial Basis Function (RBF) neural network was proposed for course control of ship steering. The traditional PID control has a wide application on ship steering fields because of its simple structure and obvious effect. But uncertainty of ship models and the disturbance of real-world environments reduce its control precision. Neural network has better control effect for such non-linear external disturbance system. Through the ability of the neural network to approximate arbitrary nonlinear to adjusting the PID three parameters in real time to achieve optimum PID control, to overcome the impact due to model uncertainty and disturbance, to achieve the purpose of automatic tracking of ship course. Through the Matlab Simulink environment simulation to verify the control effect of the traditional PID control and the RBF neural network PID control under the sinusoidal reference signal. The comparison of simulation results that PID control that adding neural network can track the reference signal more effectively, so it can achieve more precise control of the ship steering.