Modified minimal resource allocating network for ship motion predictive control

Yin, Jianchuan; Zhang, Wenjun; Li, Tieshan; Hu, Jiangqiang

doi:10.1109/icicip.2010.5565241

Cited by 2 publications

(2 citation statements)

References 17 publications

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“…Zhang and Liu used a single layer feedforward network (SLFN) to predict the heading angle of a vessel a few sample intervals ahead [18]. This one-layer prediction network is common in the literature, although the choice of activation function, training method, number of hidden neurons, type and number of input variables and the number of input lags vary greatly.…”

Section: B Data-based Motion Predictionmentioning

confidence: 99%

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

Skulstad

Fossen

et al. 2021

IEEE Trans. Instrum. Meas.

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While automatic controllers are frequently used during transit operations and low-speed maneuvering of ships, ship operators typically perform docking maneuvers. This task is more or less challenging depending on factors such as local environment disturbances, number of nearby vessels, and the speed of the ship as it docks. This paper proposes a tool for onboard support that offers position predictions based on an integration of a supervised machine learning (ML) model of the ship into the ship dynamic model. The ML model is applied as a compensator of the unmodelled behaviour or inaccuracies from the dynamic model. The dynamic model increases the amount of predetermined knowledge about how the vessel is likely to move and thus reduces the black-box factor typically experienced in purely data-driven predictors. A prediction horizon of 30 seconds ahead of real time during docking operations is examined. History data from the 29meter coastal displacement ship RV (Research Vessel) Gunnerus is applied to validate the approach. Results show that the inclusion of the data-based ML model significantly improves the prediction accuracy.

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Section: B Data-based Motion Predictionmentioning

confidence: 99%

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

Skulstad

Fossen

et al. 2021

IEEE Trans. Instrum. Meas.

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show abstract

“…The Radial Basis Function (RBF) Neural Network (NN) used in their study was updated in a sequential manner. A similar sequential RBF network was used for multi-step predictions in relation to predictive control of a ship's course in Yin et al (2010). Recently, recurrent networks have also been used for predicting roll/pitch angles and heave motion Zhang et al (2020) Duan et al (2019) and horizontal motion Skulstad et al (2019).…”

Section: Related Workmentioning

confidence: 99%

A Co-operative Hybrid Model For Ship Motion Prediction

Skulstad¹,

Li²,

Fossen³

et al. 2021

MIC

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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.

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Modified minimal resource allocating network for ship motion predictive control

Cited by 2 publications

References 17 publications

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

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

A Co-operative Hybrid Model For Ship Motion Prediction

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