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

Mei, Bin; Sun, Licheng; Shi, Guangming

doi:10.1109/access.2019.2914120

Cited by 31 publications

(10 citation statements)

References 41 publications

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“…In addition, it is gaining increasing attention to compensate the error made by a model-based predictor by using blackbox models. Mei et al [20] proposed a hybrid model of a reference vessel model and a random forest. First, their algorithm searches a reference vessel of which particulars are similar to those of a targeting vessel in their database; then the random forest is trained so that it compensates the error between the true acceleration of the targeting vessel and the predicted acceleration based on the reference vessel model.…”

Section: Hybrid Predictormentioning

confidence: 99%

A Multiple-Output Hybrid Ship Trajectory Predictor With Consideration for Future Command Assumption

Kanazawa

Skulstad

et al. 2021

IEEE Sensors J.

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Onboard sensors contribute to data-driven understanding of complex and nonlinear ship dynamics in real time. By using sensors, precise ship trajectory prediction plays a key role in intelligent collision avoidance. A hybrid predictor makes prediction based on a mathematical model of which error is compensated by a black-box model. A Multiple-output Hybrid Predictor (MHP), which makes a long-horizon prediction at a time based on onboard sensor data, was developed in the previous study. However, it can not handle a time series of future command assumption. This limitation hinders an MHP from being applied to the evaluation of future command assumption in the predictive decision making. A novel architecture of MHP presented in this study converts a long time series of future command assumption into a fixed-length model-based-predicted vessel state; then, it is included in inputs of a black-box error compensator. This idea is robust to multidimensionality of commands and long control horizon. Assuming a low-fidelity vessel model and a limited data are available, simulation experiments are conducted. The effect of environmental disturbances and maneuverings on the prediction performance is examined comprehensively for the first time. The present study successfully incorporates a long time series of future command assumption in an MHP. It reduces the mean error by 81.8% compared to a model-based predictor and by 45.6% compared to a data-driven predictor under Beaufort wind force scale 4 wave, wind, and ocean current. Present study expands the application of MHP to the predictive decision making of future autonomous ships.

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Section: Hybrid Predictormentioning

confidence: 99%

A Multiple-Output Hybrid Ship Trajectory Predictor With Consideration for Future Command Assumption

Kanazawa

Skulstad

et al. 2021

IEEE Sensors J.

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“…Minimizing the Hausdorff metric with the genetic algorithm (GA) can alleviate the impact of noise-induced problems [14]. Mei et al [19] introduced model reference and random forest (RM-RF) to model ship dynamic model and validated the proposed scheme with free-running test data. Wang et al [20] presented nu-Support Vector Machine (v-SVM) to improve the robustness of the algorithm.…”

Section: Introductionmentioning

confidence: 99%

Dynamic Model Identification of Ships and Wave Energy Converters Based on Semi-Conjugate Linear Regression and Noisy Input Gaussian Process

Liu

Xue

Huang

et al. 2021

JMSE

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Reducing the carbon emissions of ships and increasing the utilization of marine renewable energy are the important ways to achieve the goal of carbon neutrality in ocean engineering. Establishing an accurate mathematical model is the foundation of simulating the motion of marine vehicles and structures, and it is the basis of operation energy efficiency optimization and prediction of power generation. System identification from observed input–output data is a practical and powerful method. However, for modeling objects with different characteristics and known information, a single modeling framework can hardly meet the requirements of model establishment. Moreover, there are some challenges in system identification, such as parameter drift and overfitting. In this work, three robust methods are proposed for generating ocean hydrodynamic models based on Bayesian regression. Two Bayesian techniques, semi-conjugate linear regression and noisy input Gaussian process regression are used for parametric and nonparametric gray-box modeling and black-box modeling. The experimental free-running tests of the KRISO very large crude oil carrier (KVLCC2) ship model and a multi-freedom wave energy converter (WEC) are used to validate the proposed Bayesian models. The results demonstrate that the proposed schemes for system identification of the ship and WEC have good generalization ability and robustness. Finally, the developed modeling methods are evaluated considering the aspects required conditions, operating characteristics, and prediction accuracy.

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“…Minimizing the Hausdorff metric with the genetic algorithm (GA) can alleviate the impact of noise-induced problems [10]. Mei et al [15] introduced model reference and random forest (RM-RF) to model ship dynamic model and validated the proposed scheme with free-running test data. Wang et al [16] presented nu-Support Vector Machine (v-SVM) to improve the robustness of the algorithm.…”

Section: Introductionmentioning

confidence: 99%

Gray- and Black-Box Modeling of Ships and Wave Energy Converters Based on Bayesian Regression

Liu¹,

Xue²,

Huang³

et al. 2020

Preprint

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Establishing an accurate mathematical model is the foundation of simulating the motion of marine vehicles and structures, and it is the basis of modeling-based control design. System identification from observed input-output data is a practical and powerful method. However, for modeling objects with different characteristics and known information, a single modeling framework can hardly meet the requirements of model establishment. Moreover, there are some challenges in system identification, such as parameter drift and overfitting. In this work, three robust methods are proposed for generating ocean hydrodynamic models based on Bayesian regression. Two Bayesian techniques, semi-conjugate linear regression and noisy input Gaussian process regression, are used for parametric and nonparametric gray-box modeling and black-box modeling. The experimental free-running tests of the KVLCC2 ship model and a multi-freedom wave energy converter (WEC) are used to validate the proposed Bayesian models. The results demonstrate that the proposed schemes for system identification of the ship and WEC have good generalization ability and robustness. Finally, the developed modeling methods are evaluated considering the aspects required conditions, operating characteristics and prediction accuracy.

show abstract

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

Cited by 31 publications

References 41 publications

A Multiple-Output Hybrid Ship Trajectory Predictor With Consideration for Future Command Assumption

A Multiple-Output Hybrid Ship Trajectory Predictor With Consideration for Future Command Assumption

Dynamic Model Identification of Ships and Wave Energy Converters Based on Semi-Conjugate Linear Regression and Noisy Input Gaussian Process

Gray- and Black-Box Modeling of Ships and Wave Energy Converters Based on Bayesian Regression

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