Open Access Repository

Unscented Kalman Filter trained neural networks based rudder roll stabilization system for ship in waves

Wang, Yuanyuan, Chai, S ORCID: 0000-0001-5186-4456, Khan, F ORCID: 0000-0002-5638-4299 and Nguyen, HD ORCID: 0000-0003-0118-8597 2017 , 'Unscented Kalman Filter trained neural networks based rudder roll stabilization system for ship in waves' , Applied Ocean Research, vol. 68 , pp. 26-38 , doi: 10.1016/j.apor.2017.08.007.

Full text not available from this repository.

Abstract

The large roll motion of ships sailing in the seaway is undesirable because it may lead to the seasickness of crew and unsafety of vessels and cargoes, thus it needs to be reduced. The aim of this study is to design a rudder roll stabilization system based on Radial Basis Function Neural Network (RBFNN) control algorithm for ship advancing in the seaway only through rudder actions. In the proposed stabilization system, the course keeping controller and the roll damping controller were accomplished by utilizing modified Unscented Kalman Filter (UKF) training algorithm, and implemented in parallel to maintain the orientation and reduce roll motion simultaneously. The nonlinear mathematical model, which includes manoeuvring characteristics and wave disturbances, was adopted to analyse ship’s responses. Various sailing states and the external wave disturbances were considered to validate the performance and robustness of the proposed roll stabilizer. The results indicate that the designed control system per-forms better than the Back Propagation (BP) neural networks based control system and conventional Proportional-Derivative (PD) based control system in terms of reducing roll motion for ship in waves.

Item Type: Article
Authors/Creators:Wang, Yuanyuan and Chai, S and Khan, F and Nguyen, HD
Keywords: rudder roll stabilization system, neural networks, unscented Kalman filter training, trajectory tracking
Journal or Publication Title: Applied Ocean Research
Publisher: Elsevier Sci Ltd
ISSN: 0141-1187
DOI / ID Number: 10.1016/j.apor.2017.08.007
Copyright Information:

Copyright 2017 Elsevier Ltd.

Related URLs:
Item Statistics: View statistics for this item

Actions (login required)

Item Control Page Item Control Page
TOP