Song, Q.H. and Wang, D.D., 2020. Neural network ship PID control and simulation based on grey prediction. In: Al-Tarawneh, O. and Megahed, A. (eds.), Recent Developments of Port, Marine, and Ocean Engineering. Journal of Coastal Research, Special Issue No. 110, pp. 299–303. Coconut Creek (Florida), ISSN 0749-0208.

Traditional PID is difficult to be applied in large inertial system. It is determined by a large number of engineering experiments, which brings great limitations to the practical application of PID; and the traditional PID control algorithm cannot be applied to the load change, so the control results is always not good enough to be used in the precision requirement. In the paper, the grey prediction control and neural network are combined, and a neural network ship PID control strategy based on grey prediction is proposed, since the grey prediction algorithm needs a small amount of data and small computation load. The simulation results show that the new ship PID self-tuning algorithm is feasible and effective. It can be used in the change of the target parameters, and has strong anti-interference and reliability.