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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 Type:	Article
Authors/Creators:	Wang, Y and Chai, S and Nguyen, HD
Keywords:	neural networks, extended Kalman filter training, free running experiment, model scaled vessel
Journal or Publication Title:	International Journal of Naval Architecture and Ocean Engineering
Publisher:	Society of Naval Architects of Korea
ISSN:	2092-6782
DOI / ID Number:	10.1016/j.ijnaoe.2019.11.004
Copyright Information:	© 2020 Society of Naval Architects of Korea. Production and hosting by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
Related URLs:	Google Scholar: Chai, S UTAS Profile: Chai, S Google Scholar: Nguyen, HD UTAS Profile: Nguyen, HD
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