Maneuverability prediction of ship nonlinear motion models based on parameter identification and optimization

• Published in: Measurement : journal of the International Measurement Confederation Vol. 236; p. 115033
• Main Authors: liu, Yang, An, Shun, Wang, Longjin, Liu, Peng, Deng, Fang, Liu, Shanyu, Wang, Zhiyang, Fan, Zhimin
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 15.08.2024
• Subjects: Improved Gray wolf optimization; Maximum likelihood theory; Multi-innovation method; Online parameter identification; Ship maneuverability prediction; Multi-innovation method; Improved Gray wolf optimization; Ship maneuverability prediction; Online parameter identification; Maximum likelihood theory
• ISSN: 0263-2241
• DOI: 10.1016/j.measurement.2024.115033

Ship maneuverability prediction accuracy depends on the accuracy of ship motion model parameter identification. To solve the problem of parameter identification of nonlinear ship motion model, this paper proposes a online parameter identification algorithm of maximum likelihood multi-innovation recursive least squares (ML-MI-RLS) for ship motion model parameter identification. To solve the parameter drift phenomenon, the improved gray wolf optimization (IGWO) algorithm is proposed to optimize the parameter identification results. The combination of system identification and intelligent optimization algorithm not only solves the parameter drift problem of system identification, but also compensates for the lack of real-time performance of existing algorithms. The effectiveness of the ML-MI-RLS algorithm is verified by parameter identification simulations. The online identification performance of the algorithm is verified by varying the ship maneuverability parameters simulation. The proposed method is verified to have excellent performance by ship maneuverability prediction simulation. •This study combines system identification with intelligent algorithms to solve the ship parameter identification problem.•An ML-MI-RLS algorithm is proposed for ship motion model parameter identification.•An Improved Gray Wolf optimization algorithm is proposed to improve the parameter drift phenomenon.•Simulation data validate the effectiveness of the proposed method.