Toggle Main Menu Toggle Search

Open Access padlockePrints

Vertical Motion Control of Twin-Hull Vessels Using Neural Optimal Control

Lookup NU author(s): Dr Farhad Kenevissi, Professor Mehmet Atlar, Professor Ehsan Mesbahi

Downloads

Full text for this publication is not currently held within this repository. Alternative links are provided below where available.


Abstract

This paper presents a novel Neural Optimal Controller (NOC) to improve the heave and pitch motion responses of a twin-hull vessel operating in regular head seas. A practical time domain model is used to simulate the motion responses of the vessel in the presence of a pair of actively controlled forward and aft fins. Initially an On-line Switching procedure is introduced to govern a number of Linear Quadratic Regulator (LQR) optimal controllers, designed for different operating conditions of the vessel, to improve the system robustness. Although the on-line switching offered better robustness and performance characteristics, in between switching operating points, it still remained sub-optimal. Therefore, an Artificial Neural Network (ANN) controller has been proposed as an alternative. The ANN has been initially trained to emulate the same level of control at a number of design operating points and implemented on benchmark test vessel, SWATH6A, as a NOC. The advantage of this novel application is that the on-line switching procedure is no longer required and more importantly, the ANN has been capable of non-linear generalisation to give a near optimal solution away from the trained operating conditions.


Publication metadata

Author(s): Kenevissi F, Atlar M, Mesbahi E

Publication type: Conference Proceedings (inc. Abstract)

Publication status: Published

Conference Name: FAST 2001: the 6th International Conference on Fast Sea Transportation

Year of Conference: 2001

Number of Volumes: 3

Pages: 271-281

Publisher: Royal Institution of Naval Architects

Notes: "This conference supported by IZAR"

Library holdings: Search Newcastle University Library for this item

ISBN: 0903055708


Share