Nonparametric Modeling and Control of Ship Steering Motion Based on Local Gaussian Process Regression

Ouyang, Zi-Lu; Zou, Zao-Jian; Zou, Lu

doi:10.3390/jmse11112161

Cited by 2 publications

(2 citation statements)

References 24 publications

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“…The multioutput LSSVR considers the correlation and differences among four-DOF state variables. The prior information of nonparametric modelling includes only training data and input-output features [37]. The training data are collected through onboard sensors from free-running model tests or full-scale trials.…”

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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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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“…The performance of ship motion control systems directly impacts shipping safety and economic costs. Therefore, an accurate ship maneuvering model is essential to ensure optimal control system performance [3,4]. Given that a ship is a complex system with time-varying nonlinearity, its model parameters change with variations in load, draft, speed, etc.…”

Section: Introductionmentioning

confidence: 99%

Identification of Multi-Innovation Stochastic Gradients with Maximum Likelihood Algorithm Based on Ship Maneuverability and Wave Peak Models

Liu,

Zhang,

Wang

et al. 2024

JMSE

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This paper investigates the problem of real-time parameter identification for ship maneuvering parameters and wave peak frequency in an ocean environment. Based on the idea of Euler discretion, a combined model of ship maneuvering and wave peak frequency (ship–wave) is made a discretion, and a discrete-time auto-regressive moving-average model with exogenous input (ARMAX) is derived for parameter identification. Based on the ideas of stochastic gradient identification and multi-innovation theory, a multi-innovation stochastic gradient (MI-SG) algorithm is derived for parameter identification of the ship–wave discretion model. Maximum likelihood theory is introduced to propose a maximum likelihood-based multi-innovation stochastic gradient (ML-MI-SG) algorithm. Compared to the MI-SG algorithm, the ML-MI-SG algorithm shows improvements in both parameter identification accuracy and identification convergence speed. Simulation results verify the effectiveness of the proposed algorithm.

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Nonparametric Modeling and Control of Ship Steering Motion Based on Local Gaussian Process Regression

Cited by 2 publications

References 24 publications

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

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

Identification of Multi-Innovation Stochastic Gradients with Maximum Likelihood Algorithm Based on Ship Maneuverability and Wave Peak Models

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