PID Control For Ship Steering Based On Particle Swarm Optimization

This thesis has systematically researched several control strategies, such as PID control, the particle swarm optimization algorithm and ship motion mathematic model, and their applications to ship's course steering control.A novel particle swarm optimization algorithm with adaptive linear decreasing search space has been proposed, after the presentation of particle swarm optimization algorithm. The algorithm implement adaptive inertia weight for different levels of particles'fitness, and shrink the search space during the running time. The best part of particles are chosen and propagate directly to the next generation of particles, the others are randomly generated within the reduced search space. The approach can not only speed up the convergence rate of population, but also enable the population jump out of the local minimum. The experiment results demonstrate that the algorithm has improved convergence speed and solution accuracy.A inverted pendulum system PID control based on the above particle swarm optimization algorithm is designed. Simulation research shows that the system based on the particle swarm optimization algorithm with shorter time to stability, the control curve is more stable, the algorithm can be used to the control field.Finally, a ship course steering autopilot is designed, based on the above particle swarm optimization algorithm. Simulation research shows that PID control for ship's course steering based on particle swarm optimization has larger ascension, the system without overshoot, rising fast, stable work, and with strong robust performance.