Modeling of ship maneuvering motion using neural networks

Luo, Weilin; Zhang, Zhicheng

doi:10.1007/s11804-016-1380-8

Cited by 32 publications

(5 citation statements)

References 16 publications

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“…Mathematical models of ships' maneuvering motion include the models mentioned above. Common parameter identification methods include least squares [81,82], Kalman filtering [83,84], support vector machines [85,86], neural networks [87,88], least squares support vector machine methods [89][90][91], particle swarm optimization algorithms [92,93], and Bayesian methods [94,95], among others. Several scholars have conducted in-depth research on this [96][97][98].…”

Section: Ship Extreme Short-term Motion Predictionmentioning

confidence: 99%

A Review on Motion Prediction for Intelligent Ship Navigation

Zhang,

Chu,

Liu

et al. 2024

JMSE

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In recent years, as intelligent ship-navigation technology has advanced, the challenge of accurately modeling and predicting the dynamic environment and motion status of ships has emerged as a prominent area of research. In response to the diverse time scales required for the prediction of ship motion, various methods for modeling ship navigation environments, ship motion, and ship traffic flow have been explored and analyzed. Additionally, these motion-prediction methods are applied for motion control, collision-avoidance planning, and route optimization. Key issues are summarized regarding ship-motion prediction, including online modeling of motion models, real ship validation, and consistency in modeling, optimization, and control. Future technology trends are predicted in mechanism-data fusion modeling, large-scale model, multi-objective motion prediction, etc.

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Section: Ship Extreme Short-term Motion Predictionmentioning

confidence: 99%

A Review on Motion Prediction for Intelligent Ship Navigation

Zhang,

Chu,

Liu

et al. 2024

JMSE

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“…In almost all SI studies on the ship maneuvering models using NN, the target is basically to maneuver with positive longitudinal speed, and maneuver modes are zigzag and turning tests [29,[31][32][33]. In [30], the propeller RPS was constant.…”

Section: Subject Ship and Experimentsmentioning

confidence: 99%

“…Many studies have been conducted on SI for ship maneuvers using black-box models, such as support vector machine [24][25][26][27], random forest [28], and neural networks (NNs) [29][30][31][32][33][34][35]. In particular, an NN can approximate various functions using a set of the appropriate number of parameters, as shown in the universal approximation theorem (UAT) [36][37][38], and the gradient can easily be calculated using the backpropagation (BP) method [39].…”

Section: Introductionmentioning

confidence: 99%

“…To train an NN model for low-speed manuevering, both combinations of positive/negative propeller revolution per second (RPS) and ship-positive/negative velocity should be included in the training datasets. In almost all SI studies on ship maneuvering models using NNs [29,[31][32][33], the target is the maneuvering motion with positive speed and positive propeller revolution number, and the maneuver scenarios are mainly "usual maneuver," such as zigzag and turning tests. In [30], the propeller revolution number was constant.…”

Section: Introductionmentioning

confidence: 99%

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On Neural Network Identification for Low-Speed Ship Maneuvering Model

Wakita,

Maki,

Naoya

et al. 2021

Preprint

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Several studies on ship maneuvering models have been conducted using captive model tests or computational fluid dynamics (CFD) and physical models, such as the maneuvering modeling group (MMG) model. A new system identification method for generating a low-speed maneuvering model using recurrent neural networks (RNNs) and free running model tests is proposed in this study. We especially focus on a low-speed maneuver such as the final phase in berthing to achieve automatic berthing control. Accurate dynamic modeling with minimum modeling error is highly desired to establish a model-based control system. We propose a new loss function that reduces the effect of the noise included in the training data. Besides, we revealed the following facts -an RNN that ignores the memory before a certain time improved the prediction accuracy compared with the "standard" RNN, and the random maneuver test was effective in obtaining an accurate berthing maneuver model. In addition, several low-speed free running model tests were performed for the scale model of the M.V. Esso Osaka. As a result, this paper showed that the proposed method using a neural network model could accurately represent low-speed maneuvering motions.

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“…In addition, Yoon and Rhee proposed the D-type ship test scheme [22]. Luo and Zhang presented a two-layer forward NN to identify the parameters of a linear model, and the neural network was similar to nonlinear approximation [23]. Recently, Luo and Li reduced parameter drift by means of SVM sample reconstruction [24].…”

Section: A Survey Of Ship Maneuvering Identification Modelingmentioning

confidence: 99%

White-Black-Box Hybrid Model Identification Based on RM-RF for Ship Maneuvering

2019

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A new system identification scheme based on model reference and random forest (RM-RF), was used to model ship maneuvering. First, the scheme establishes the relationship between a ship and the RM using the similarity rule. Second, a suitable RM was selected from public ship maneuvering models to avoid the tuning process in the empirical Maneuvering Modeling Group (MMG), model. Third, RF creates a relationship to map accelerations between the ship and the RM. Finally, the study case was implemented with fewer free-running model test data. The results show the feasibility of the identification modeling scheme and validated the generalizability of the proposed approach.

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Modeling of ship maneuvering motion using neural networks

Cited by 32 publications

References 16 publications

A Review on Motion Prediction for Intelligent Ship Navigation

A Review on Motion Prediction for Intelligent Ship Navigation

On Neural Network Identification for Low-Speed Ship Maneuvering Model

White-Black-Box Hybrid Model Identification Based on RM-RF for Ship Maneuvering

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