Physics-informed identification of marine vehicle dynamics using hydrodynamic dictionary library-inspired adaptive regression

Liu, Ang; Xue, Yifan; Qin, Hongde; Zhu, Zhongben

doi:10.1016/j.oceaneng.2024.117013

Cited by 3 publications

(1 citation statement)

References 34 publications

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“…SI does not require complex tank experiments to obtain hydrodynamic derivative values but instead models using data collected from free-running model tests or full-scale trials [11]. Parametric modelling is a direct and widely used method of SI modelling [12,13]. To capture the six-degree-of-freedom (DOF) dynamics of ships, Zhu et al [14] designed a simplified parametric model.…”

Section: Introductionmentioning

confidence: 99%

Nonparametric Modelling of Ship Dynamics Using Puma Optimizer Algorithm-Optimized Twin Support Vector Regression

Jiang,

Zhang,

et al. 2024

JMSE

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Ship dynamic models serve as the foundation for designing ship controllers, trajectory planning, and obstacle avoidance. Support vector regression (SVR) is a commonly used nonparametric modelling method for ship dynamics. Achieving high accuracy SVR models requires a substantial amount of training samples. Additionally, as the number of training samples increases, the computational efficiency for solving the quadratic programming problem (QPP) of SVR decreases. Ship controllers demand dynamic models with both high accuracy and computational efficiency. Therefore, to enhance the prediction accuracy and computational efficiency of SVR, this paper proposes a nonparametric modelling method based on twin SVR (TSVR). TSVR replaces a large QPP with a set of smaller QPPs, significantly enhancing generalizability and computational efficiency. To further improve the predictive accuracy of TSVR, the puma optimizer algorithm is employed to determine the optimal hyperparameters. The performance of the proposed method is validated using a Mariner class vessel. Gaussian white noise is introduced into the modelling data to simulate measurement error. The TSVR model accurately predicts various zigzag and turning circle manoeuvring motions under disturbance conditions, demonstrating its robustness and generalizability. Compared to the SVR model, the TSVR model achieves lower root mean square error and computational time, confirming its superior predictive accuracy and computational efficiency.

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