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Experimental and numerical study of autopilot using Extended Kalman Filter trained neural networks for surface vessels

Citation

Wang, Y and Chai, S and Nguyen, HD, Experimental and numerical study of autopilot using Extended Kalman Filter trained neural networks for surface vessels, International Journal of Naval Architecture and Ocean Engineering, 12, (2020) pp. 314-324. ISSN 2092-6782 (2020) [Refereed Article]


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DOI: doi:10.1016/j.ijnaoe.2019.11.004

Abstract

Due to the nonlinearity and environmental uncertainties, the design of the ship's steering controller is a long-term challenge. The purpose of this study is to design an intelligent autopilot based on Extended Kalman Filter (EKF) trained Radial Basis Function Neural Network (RBFNN) control algorithm. The newly developed free running model scaled surface vessel was employed to execute the motion control experiments. After describing the design of the EKF trained RBFNN autopilot, the performances of the proposed control system were investigated by conducting experiments using the physical model on lake and simulations using the corresponding mathematical model. The results demonstrate that the developed control system is feasible to be used for the ship's motion control in the presences of environmental disturbances. Moreover, in comparison with the Back-Propagation (BP) neural networks and Proportional-Derivative (PD) based control methods, the EKF RBFNN based control method shows better performance regarding course keeping and trajectory tracking.

Item Details

Item Type:Refereed Article
Keywords:neural networks, extended Kalman filter training, free running experiment, model scaled vessel
Research Division:Engineering
Research Group:Maritime engineering
Research Field:Ship and platform structures (incl. maritime hydrodynamics)
Objective Division:Transport
Objective Group:Water transport
Objective Field:Water transport not elsewhere classified
UTAS Author:Wang, Y (Mr Yuanyuan Wang)
UTAS Author:Chai, S (Professor Shuhong Chai)
UTAS Author:Nguyen, HD (Dr Hung Nguyen)
ID Code:150768
Year Published:2020
Web of Science® Times Cited:5
Deposited By:NC Maritime Engineering and Hydrodynamics
Deposited On:2022-06-29
Last Modified:2022-06-29
Downloads:0

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