A Hybrid Approach to Motion Prediction for Ship Docking—Integration of a Neural Network Model Into the Ship Dynamic Model

Skulstad, Robert; Li, Guoyuan; Fossen, Thor I.; Vik, Bjørnar; Zhang, Houxiang

doi:10.1109/tim.2020.3018568

Cited by 72 publications

(42 citation statements)

References 32 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The most applied models are neural networks, and examples of predicting ship responses are reported in [12], [13]. Besides, the long short-term memory deep neural network is also popular when dealing with time series predictions either as an end-to-end model [14] or a compensative model [15]. An example of utilizing clustering techniques is presented in [16].…”

Section: B Data-driven Predictionmentioning

confidence: 99%

Incorporating Approximate Dynamics Into Data-Driven Calibrator: A Representative Model for Ship Maneuvering Prediction

Wang

Hatledal

et al. 2022

IEEE Trans. Ind. Inf.

Self Cite

View full text Add to dashboard Cite

High-fidelity models capable of accurately predicting ship motion are critical for promoting innovation and efficiency in the maritime industry. However, creating an advanced model that comprehensively represents the system and its interaction with dynamic environments has always been challenging. Many models provide partial knowledge about a system. To handle the deficiency and improve model fidelity, we propose a hybrid modeling methodology, in which prior knowledge describing the ship dynamic effects is incorporated into a data-driven calibrator, yielding a representative model with high predictive capability. Enabled by the integration of model estimated ship states into the calibrator, the informative information could be interpreted and carried forward. Simulation and full-scale experiments are conducted on the research vessel Gunnerus to exemplify the concept. A best available numerical model and a neural network are prepared to be the foundation and calibrator, respectively. Experiment results show that the cooperative model greatly improves the predictive capability of the research vessel. From the ship modeling perspective, this study provides new insights by bridging the gap between two separate domains: model-based and data-driven.

show abstract

Section: B Data-driven Predictionmentioning

confidence: 99%

Incorporating Approximate Dynamics Into Data-Driven Calibrator: A Representative Model for Ship Maneuvering Prediction

Wang

Hatledal

et al. 2022

IEEE Trans. Ind. Inf.

Self Cite

View full text Add to dashboard Cite

show abstract

“…Training efficiency of the data-based predictor may be enhanced by predicting residual errors in the dynamic model predictions. And those residual predictions may be used to account for the previously mentioned fidelity issues Skulstad et al (2021).…”

Section: Related Workmentioning

confidence: 99%

“…Albeit, with a focus of improving parameter estimates of a partially known model. The present study focuses on compensating the predictions made by a model which is assumed to be complete, but lacking information about the complete environmental disturbances Skulstad et al (2021).…”

Section: Introductionmentioning

confidence: 99%

A Co-operative Hybrid Model For Ship Motion Prediction

Skulstad¹,

Li²,

Fossen³

et al. 2021

MIC

Self Cite

View full text Add to dashboard Cite

Dynamic models of ships have been widely used for model-based control and short-term prediction in the past. Identifying the parameters of such models has mainly been done through scaled model tests, full scale tests or computational fluid dynamics software. This is a challenging task due to the many aspects that influence the ship dynamic behaviour and thus one would expect a certain degree of mismatch between the actual motion of the ship and the modelled behaviour. The mismatch in the dynamic model may be due to unmodelled effects, but also the lack of measurements of waves and ocean current. To make up for the discrepancies the authors propose to create a co-operative hybrid model consisting of the dynamic model and a neural network, where the neural network predicts the acceleration error of the dynamic model. The approach is tested on real data originating from the Research Vessel (RV) Gunnerus performing a shutdown of thrusters during stationkeeping. The subsequent task is to predict the propagation of position and heading while drifting due to wind, wave and current forces. Comparing the motion of the real ship and the modelled ship, shows the improved prediction accuracy of the hybrid model.

show abstract

“…The effectiveness of neural networks for time-series prediction tasks in other fields, such as energy efficiency [14], multi-robot systems [15], and autonomous electric vehicles [16], has been validated. Neural networks, such as simple backpropagation neural networks [17], recurrent neural networks [18], radial basis function neural networks [1] [19], echo state networks [20], extreme learning machines [21], and hybrid models [22] [23], have also been widely utilized in ship motion prediction.…”

Section: Introductionmentioning

confidence: 99%