Research and comparison of automatic control algorithm for unmanned ship

In today's extremely competitive shipping era, especially in the field of unmanned ships, the safety of ship navigation and the rapid growth of operational demand have forced ship designers to improve the automatic control performance of ships, in which ships Heading control system is most important. PID is an integral part of ship heading control system. However, the three parameters Ki, Kp and Kd of traditional PID need to be manually set and can't be adjusted in real time, which lacks flexibility. For a ship with such a large inertia device, PID control easily lead to serious overshoot and deviation. Therefore, this paper separately studies the performance of vanilla PID algorithm, fuzzy PID algorithm and back propagation (BP) neural network PID algorithm applied to unmanned ship autopilot. The three parameters can be adjusted in real time by combining Fuzzy algorithm and neural network. The simulation results show that the performance of neural network PID is better than the former two controllers in both cases of single command and continuous tracking. In the case of adjusted by a single command, the overshoot is only 41% of the PID control, and the adjustment time is only PID control 27%, so improved PID controller based on neural network has a bright future.