Neural network predictive control of a deep submergence rescue vehicle

Underwater vehicles consist of robotic vehicles that have been developed to reduce the risks of human life and to carry out tasks that would be impractical with a manned mission. The design of a depth control of an underwater vehicle is described in this thesis. For an underwater vehicle the most im...

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Main Author:	Mohd. Nor, Arfah Syahida
Format:	Thesis
Published:	2010
Subjects:	TK Electrical engineering Electronics Nuclear engineering
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id	my-utm-ep.26872
record_format	uketd_dc
spelling	my-utm-ep.268722017-08-16T09:11:42Z Neural network predictive control of a deep submergence rescue vehicle 2010 Mohd. Nor, Arfah Syahida TK Electrical engineering. Electronics Nuclear engineering Underwater vehicles consist of robotic vehicles that have been developed to reduce the risks of human life and to carry out tasks that would be impractical with a manned mission. The design of a depth control of an underwater vehicle is described in this thesis. For an underwater vehicle the most important one is that the vehicle must be stable throughout its entire operational range. In this project, the modelling and design of the depth control systems for a small underwater vehicle will be described. Three types of controllers are discussed here which include PD, Fuzzy Logic and Neural Network Predictive Control. These techniques have the purpose of ensuring zero steady state error and minimum error in response to step commands in the desired depth. Through this project, the interaction between motions in the vertical plane due to the stem plane deflections had been discussed. The neural network gives satisfactory results. However, the design is complex because there are a large number of parameters that needs to be tuned. Based on these criteria, a comparison study is performed and all of the controllers are found to give satisfactory results. The proposed Neural Network Predictive Control exactly gives the better result in terms of transient and steady state response which shows the effectiveness of the designed controller. 2010 Thesis http://eprints.utm.my/id/eprint/26872/ http://libraryopac.utm.my/client/en_AU/main/search/results?qu=Neural+network+predictive+control+of+a+deep+submergence+rescue+vehicle&te= masters Universiti Teknologi Malaysia, Faculty of Electrical Engineering Faculty of Electrical Engineering
institution	Universiti Teknologi Malaysia
collection	UTM Institutional Repository
topic	TK Electrical engineering Electronics Nuclear engineering
spellingShingle	TK Electrical engineering Electronics Nuclear engineering Mohd. Nor, Arfah Syahida Neural network predictive control of a deep submergence rescue vehicle
description	Underwater vehicles consist of robotic vehicles that have been developed to reduce the risks of human life and to carry out tasks that would be impractical with a manned mission. The design of a depth control of an underwater vehicle is described in this thesis. For an underwater vehicle the most important one is that the vehicle must be stable throughout its entire operational range. In this project, the modelling and design of the depth control systems for a small underwater vehicle will be described. Three types of controllers are discussed here which include PD, Fuzzy Logic and Neural Network Predictive Control. These techniques have the purpose of ensuring zero steady state error and minimum error in response to step commands in the desired depth. Through this project, the interaction between motions in the vertical plane due to the stem plane deflections had been discussed. The neural network gives satisfactory results. However, the design is complex because there are a large number of parameters that needs to be tuned. Based on these criteria, a comparison study is performed and all of the controllers are found to give satisfactory results. The proposed Neural Network Predictive Control exactly gives the better result in terms of transient and steady state response which shows the effectiveness of the designed controller.
format	Thesis
qualification_level	Master's degree
author	Mohd. Nor, Arfah Syahida
author_facet	Mohd. Nor, Arfah Syahida
author_sort	Mohd. Nor, Arfah Syahida
title	Neural network predictive control of a deep submergence rescue vehicle
title_short	Neural network predictive control of a deep submergence rescue vehicle
title_full	Neural network predictive control of a deep submergence rescue vehicle
title_fullStr	Neural network predictive control of a deep submergence rescue vehicle
title_full_unstemmed	Neural network predictive control of a deep submergence rescue vehicle
title_sort	neural network predictive control of a deep submergence rescue vehicle
granting_institution	Universiti Teknologi Malaysia, Faculty of Electrical Engineering
granting_department	Faculty of Electrical Engineering
publishDate	2010
_version_	1747815531291869184

Neural network predictive control of a deep submergence rescue vehicle

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