Parameter Tuning of PID Heading Controller for Self Navigation Model Based on Improved Particle Swarm Optimization

A nonlinear dynamic weight improved particle swarm optimization algorithm is proposed to address the difficulty of manually tuning the parameters of proportional integral differential (PID) in the self navigation model heading control system and the poor performance of the parameters obtained through tuning optimization. This algorithm is applied to the tuning process of PID parameters in the self navigation model heading control system. Taking the heading control of self navigation model as the research object, the mathematical model of the heading control system of self navigation model is firstly established, and an improved particle swarm optimization method is adopted to process the inertia weight with nonlinear dynamic decline, improve the search performance and optimization efficiency, and make up for the shortcomings of the traditional particle swarm optimization method. Finally, simulation comparison and analysis are carried out through Matlab/Simulink. The experimental results show that after using the improved particle swarm optimization method for parameter tuning, the self navigation model has better dynamic performance, shorter adjustment time, and also eliminates the difficulty of manual tuning, reducing the time for parameter tuning. According to the application of the ship’s manipulation index in the mathematical model in the second order, use the Matlab and Simulink software to implement the response model to simulate the ship’s rotation trajectory and the heading angle, and compare with the ship model rotation test data.