Research on the On-Line Identification of Ship Maneuvering Motion Model Parameters and Adaptive Control

This study aims to improve control accuracy across various ship types, speeds, and external interference scenarios using conventional control methods. The ship’s maneuvering model is identified online and the identified parameters are applied for self-adaptive course and track control, laying the groundwork for intelligent ship control. A response-type ship maneuvering model is used, with a forgetting factor incorporated into the recursive least squares (RLS) algorithm based on the iterative least squares (ILS) method. This addresses the limitations of the ordinary least squares (OLS) method and the RLS algorithm’s reduced update speed with data accumulation. The forgetting factor recursive least squares (FFRLS) algorithm is employed to identify the maneuverability index parameters (K and T). Data for identification are obtained via a maneuvering simulator and the impact of different forgetting factors on the identification process is evaluated. The identified results are then used to calculate real-time optimal PID (OP-PID) parameters, leading to the development of a Self-adaptive OP-PID course control method. Simulations of course and track control are conducted with various ship types and environments, comparing the Self-adaptive OP-PID with existing OP-PID methods. Results show that the Self-adaptive OP-PID outperforms the OP-PID in course stability, convergence time, and track deviation under the same conditions.