关键字：SYSTEM

摘要：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 ying the ship maneuverability parameters simulation. The proposed method is verified to have excellent performance by ship maneuverability prediction simulation.

卷号：236

期号：-

是否译文：否