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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 KhanThis 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
158-163ISBN
978-1-9221-0769-5Department/School
Australian Maritime CollegePublisher
Engineers AustraliaPlace of publication
AustraliaEvent title
2015 5th Australian Control Conference (AUCC)Event Venue
Gold Coast, AustraliaDate of Event (Start Date)
2015-11-05Date of Event (End Date)
2015-11-06Rights statement
Copyright 2015 Engineers AustraliaRepository Status
- Restricted