Load distribution for an intelligent air-cushion track vehicle based on optimal power consumption

Hossain, Altab and Rahman, Mohammed Ataur and Mohiuddin, A. K. M. (2010) Load distribution for an intelligent air-cushion track vehicle based on optimal power consumption. International Journal of Vehicle Systems Modelling and Testing, 5 (2/3). pp. 237-253. ISSN 1745-6444 (O), 1745-6436 (P)

PDF (Journal Paper) - Published Version Restricted to Repository staff only Download (353kB) | Request a copy

Abstract

This study incorporates the computation of load distribution for an intelligent air-cushion track vehicle (IACTV) based on optimal power consumption (PC). A novel design auto adjusting air-cushion system has been designed and developed to minimise the PC. Focusing on optimising total PC,three main issues were studied in this paper. First, a theoretical model is developed for optimising total PC of the vehicle and the effects of load distribution on total PC. Second, a fuzzy expert system (FES) is used to control the swamp tracked vehicle’s intelligent air cushion system and thus minimise the PC. Third, an artificial neural network (ANN) model has been developed which has been trained to predict the total PC for IACTV and to provide illustration how FES might play an important role in the prediction of PC of the vehicle’s intelligent air-cushion system.

Item Type:	Article (Journal)
Additional Information:	5264/517
Uncontrolled Keywords:	track vehicles; load distribution; power consumption; PC; air-cushion vehicles; intelligent vehicles; fuzzy expert systems; fuzzy logic; artificial neural networks; ANNs; modelling.
Subjects:	T Technology > TL Motor vehicles. Aeronautics. Astronautics > TL1 Motor vehicles
Kulliyyahs/Centres/Divisions/Institutes (Can select more than one option. Press CONTROL button):	Kulliyyah of Engineering > Department of Mechanical Engineering
Depositing User:	Prof. Dr. A K M Mohiuddin
Date Deposited:	08 Aug 2011 11:28
Last Modified:	08 Aug 2011 11:29
URI:	http://irep.iium.edu.my/id/eprint/517

Actions (login required)

View Item