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Radial basis function neural network based rudder roll stabilization for ship sailing in waves

conference contribution
posted on 2023-05-23, 11:22 authored by Wang, Y, Hung NguyenHung Nguyen, Shuhong ChaiShuhong Chai, Faisal KhanFaisal Khan
This paper presents a rudder-roll stabilization system utilizing Radial Basis Function neural network (RBFNN) for course keeping and roll damping. Roll motion of a vessel sailing under severe weather conditions has adverse effects on crews’ health, cargoes and safety, thus it must be damped as much as possible. A new control algorithm for both course keeping and roll damping is proposed based on the RBFNNs. In order to realize the proposed rudder roll stabilization system, a nonlinear mathematical model of a container vessel with effects of wave disturbance is used to simulate the proposed rudder roll stabilization system which consists of two controllers implemented in parallel, one is the autopilot for course keeping and the other is roll damping controller. The performance and robustness of the proposed control system is investigated by taking consideration of the effects of external disturbance. The simulation studies are designed to verify the improved performance of the proposed rudder roll stabilization system and to validate its efficiency of course keeping and roll motion reduction.

History

Publication title

Proceedings of the 5th Australian Control Conference (AUCC)

Pagination

158-163

ISBN

978-1-9221-0769-5

Department/School

Australian Maritime College

Publisher

Engineers Australia

Place of publication

Australia

Event title

2015 5th Australian Control Conference (AUCC)

Event Venue

Gold Coast, Australia

Date of Event (Start Date)

2015-11-05

Date of Event (End Date)

2015-11-06

Rights statement

Copyright 2015 Engineers Australia

Repository Status

  • Restricted

Socio-economic Objectives

Automotive equipment

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