Optimal tuning of controller parameters for a magnetic levitation system using radial basis based neural network metamodeling approach

The Magnetic Levitation System (MLS) is a challenging nonlinear mechatronic system in which an electromagnetic force required to suspend an object (metal sphere) in the air. The electromagnetic force is very sensitive to the noise, which can create acceleration forces on the metal sphere, causing th...

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主要作者:	Mohammad Abdalhadi, Abdualrhman Daw
格式:	Thesis
语言:	English
出版:	2021
主题:	TK7885-7895 Computer engineer Computer hardware
在线阅读:	http://eprints.utm.my/id/eprint/97301/1/AbdualrhmanDawMohammadAbdalhadiMSEE2021.pdf.pdf
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spelling	my-utm-ep.973012022-09-26T03:58:36Z Optimal tuning of controller parameters for a magnetic levitation system using radial basis based neural network metamodeling approach 2021 Mohammad Abdalhadi, Abdualrhman Daw TK7885-7895 Computer engineer. Computer hardware The Magnetic Levitation System (MLS) is a challenging nonlinear mechatronic system in which an electromagnetic force required to suspend an object (metal sphere) in the air. The electromagnetic force is very sensitive to the noise, which can create acceleration forces on the metal sphere, causing the sphere to move into the unbalanced region. Maglev’s benefits the industry, and the system has reduced power consumption, has increased power efficiency, and reduced maintenance cost. The typical applications for Maglev’s Power Generation, for example, wind turbine, Maglev’s trains, and Medical Device (magnetically suspended Artificial Heart Pump). This project presents a comparative assessment of controllers for the magnetic levitation system and the way of optimally tune of the PID parameter. The magnetic levitation system divided into two types, attractive and repulsive, in this project attractive type has been chosen. The analysis will be performed after finding the state space model of magnetic levitation system, and simulation will be performed using MATLAB Simulink. The optimal tuning based PID controller will offer a transient response with better overshoot and rise time than the standard optimization methods. For the trained networks, metamodel radial basis function networks perform more robustly and tolerantly than the gradient descent method even when dealing with noised input data set. The simulation output using the radial basis based metamodel approach showed an overshoot of 9.34% and rise time 9.84ms, which is better than the gradient descent and conventional PID methods. 2021 Thesis http://eprints.utm.my/id/eprint/97301/ http://eprints.utm.my/id/eprint/97301/1/AbdualrhmanDawMohammadAbdalhadiMSEE2021.pdf.pdf application/pdf en public http://dms.library.utm.my:8080/vital/access/manager/Repository/vital:144545 masters Universiti Teknologi Malaysia Faculty of Engineering - School of Electrical Engineering
institution	Universiti Teknologi Malaysia
collection	UTM Institutional Repository
language	English
topic	TK7885-7895 Computer engineer Computer hardware
spellingShingle	TK7885-7895 Computer engineer Computer hardware Mohammad Abdalhadi, Abdualrhman Daw Optimal tuning of controller parameters for a magnetic levitation system using radial basis based neural network metamodeling approach
description	The Magnetic Levitation System (MLS) is a challenging nonlinear mechatronic system in which an electromagnetic force required to suspend an object (metal sphere) in the air. The electromagnetic force is very sensitive to the noise, which can create acceleration forces on the metal sphere, causing the sphere to move into the unbalanced region. Maglev’s benefits the industry, and the system has reduced power consumption, has increased power efficiency, and reduced maintenance cost. The typical applications for Maglev’s Power Generation, for example, wind turbine, Maglev’s trains, and Medical Device (magnetically suspended Artificial Heart Pump). This project presents a comparative assessment of controllers for the magnetic levitation system and the way of optimally tune of the PID parameter. The magnetic levitation system divided into two types, attractive and repulsive, in this project attractive type has been chosen. The analysis will be performed after finding the state space model of magnetic levitation system, and simulation will be performed using MATLAB Simulink. The optimal tuning based PID controller will offer a transient response with better overshoot and rise time than the standard optimization methods. For the trained networks, metamodel radial basis function networks perform more robustly and tolerantly than the gradient descent method even when dealing with noised input data set. The simulation output using the radial basis based metamodel approach showed an overshoot of 9.34% and rise time 9.84ms, which is better than the gradient descent and conventional PID methods.
format	Thesis
qualification_level	Master's degree
author	Mohammad Abdalhadi, Abdualrhman Daw
author_facet	Mohammad Abdalhadi, Abdualrhman Daw
author_sort	Mohammad Abdalhadi, Abdualrhman Daw
title	Optimal tuning of controller parameters for a magnetic levitation system using radial basis based neural network metamodeling approach
title_short	Optimal tuning of controller parameters for a magnetic levitation system using radial basis based neural network metamodeling approach
title_full	Optimal tuning of controller parameters for a magnetic levitation system using radial basis based neural network metamodeling approach
title_fullStr	Optimal tuning of controller parameters for a magnetic levitation system using radial basis based neural network metamodeling approach
title_full_unstemmed	Optimal tuning of controller parameters for a magnetic levitation system using radial basis based neural network metamodeling approach
title_sort	optimal tuning of controller parameters for a magnetic levitation system using radial basis based neural network metamodeling approach
granting_institution	Universiti Teknologi Malaysia
granting_department	Faculty of Engineering - School of Electrical Engineering
publishDate	2021
url	http://eprints.utm.my/id/eprint/97301/1/AbdualrhmanDawMohammadAbdalhadiMSEE2021.pdf.pdf
_version_	1747818696254947328

Optimal tuning of controller parameters for a magnetic levitation system using radial basis based neural network metamodeling approach

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