An Integrated Method for Ship Heading Control Using Motion Model Prediction and Fractional Order Proportion Integration Differentiation Controller

Abstract

Due to the influence of the natural environment, it is very challenging to control the movement of ships to navigate safely and avoid potential risks induced by external environmental factors, especially for the development of autonomous ships in inland or restricted waterways. In this research, we propose an integrated approach for ship heading control that improves the timeliness and robustness of navigation. Recursive least squares and backward propagation neural networks are utilized to identify the ship motion model parameters under the influence of external factors and predict their development in real time. A particle swarm optimization-integrated Fractional Order Proportion Integration Differentiation (FOPID) controller is then designed based on the dynamically identified motion model to achieve accurate heading control for ships navigating in restricted waterways. A case study was conducted based on the Korea Venture Large Crude Carrier 2 (KVLCC2) model to verify the effectiveness, and a comparison between the conventional FOPID controller and the improved FOPID controller was also conducted. The results indicate that the proposed identification–prediction–optimization FOPID controller has faster speed on stabilization and has higher robustness against external influences, which could provide added value for the development of a motion controller for the autonomous ship for inland and restricted waterway navigation.