Intelligent H2/H∞ Robust Control of an Active Magnetic Bearings System

Abstract Robust controller design requires a proper definition of uncertainty bounds. These uncertainty bounds are commonly selected randomly and conservatively for certain stability, without regard for controller performance. This issue becomes critically important for multivariable systems wi...

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Autores principales:	Safanah M. Raafat, Rini Akmeliawati
Formato:	article
Lenguaje:	EN
Publicado:	Al-Khwarizmi College of Engineering – University of Baghdad 2015
Materias:	Keywords: Active Magnetic Bearings (AMB) , Adaptive Neuro Fuzzy Inference System (ANFIS), H2/H∞ robust controller, modelling errors, uncertainty bounds. Chemical engineering TP155-156 Engineering (General). Civil engineering (General) TA1-2040
Acceso en línea:	https://doaj.org/article/1d6caa9bd52948cbb9cf25c9873e0702
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spelling	oai:doaj.org-article:1d6caa9bd52948cbb9cf25c9873e07022021-12-02T07:16:40ZIntelligent H2/H∞ Robust Control of an Active Magnetic Bearings System1818-11712312-0789https://doaj.org/article/1d6caa9bd52948cbb9cf25c9873e07022015-03-01T00:00:00Zhttp://alkej.uobaghdad.edu.iq/index.php/alkej/article/view/219https://doaj.org/toc/1818-1171https://doaj.org/toc/2312-0789 Abstract Robust controller design requires a proper definition of uncertainty bounds. These uncertainty bounds are commonly selected randomly and conservatively for certain stability, without regard for controller performance. This issue becomes critically important for multivariable systems with high nonlinearities, as in Active Magnetic Bearings (AMB) System. Flexibility and advanced learning abilities of intelligent techniques make them appealing for uncertainty estimation. The aim of this paper is to describe the development of robust H2/H∞ controller for AMB based on intelligent estimation of uncertainty bounds using Adaptive Neuro Fuzzy Inference System (ANFIS). Simulation results reveal that the robust controller design objectives of wide bandwidth and improved performance are satisfied for a wide range of frequency variations. It can be concluded that the intelligent uncertainty weighting functions can precisely compensate for the effects of modelling errors and nonlinearities in the system. Keywords: Active Magnetic Bearings (AMB) , Adaptive Neuro Fuzzy Inference System (ANFIS), H2/H∞ robust controller, modelling errors, uncertainty bounds. Safanah M. RaafatRini AkmeliawatiAl-Khwarizmi College of Engineering – University of BaghdadarticleKeywords: Active Magnetic Bearings (AMB) , Adaptive Neuro Fuzzy Inference System (ANFIS), H2/H∞ robust controller, modelling errors, uncertainty bounds.Chemical engineeringTP155-156Engineering (General). Civil engineering (General)TA1-2040ENAl-Khawarizmi Engineering Journal, Vol 11, Iss 2 (2015)
institution	DOAJ
collection	DOAJ
language	EN
topic	Keywords: Active Magnetic Bearings (AMB) , Adaptive Neuro Fuzzy Inference System (ANFIS), H2/H∞ robust controller, modelling errors, uncertainty bounds. Chemical engineering TP155-156 Engineering (General). Civil engineering (General) TA1-2040
spellingShingle	Keywords: Active Magnetic Bearings (AMB) , Adaptive Neuro Fuzzy Inference System (ANFIS), H2/H∞ robust controller, modelling errors, uncertainty bounds. Chemical engineering TP155-156 Engineering (General). Civil engineering (General) TA1-2040 Safanah M. Raafat Rini Akmeliawati Intelligent H2/H∞ Robust Control of an Active Magnetic Bearings System
description	Abstract Robust controller design requires a proper definition of uncertainty bounds. These uncertainty bounds are commonly selected randomly and conservatively for certain stability, without regard for controller performance. This issue becomes critically important for multivariable systems with high nonlinearities, as in Active Magnetic Bearings (AMB) System. Flexibility and advanced learning abilities of intelligent techniques make them appealing for uncertainty estimation. The aim of this paper is to describe the development of robust H2/H∞ controller for AMB based on intelligent estimation of uncertainty bounds using Adaptive Neuro Fuzzy Inference System (ANFIS). Simulation results reveal that the robust controller design objectives of wide bandwidth and improved performance are satisfied for a wide range of frequency variations. It can be concluded that the intelligent uncertainty weighting functions can precisely compensate for the effects of modelling errors and nonlinearities in the system. Keywords: Active Magnetic Bearings (AMB) , Adaptive Neuro Fuzzy Inference System (ANFIS), H2/H∞ robust controller, modelling errors, uncertainty bounds.
format	article
author	Safanah M. Raafat Rini Akmeliawati
author_facet	Safanah M. Raafat Rini Akmeliawati
author_sort	Safanah M. Raafat
title	Intelligent H2/H∞ Robust Control of an Active Magnetic Bearings System
title_short	Intelligent H2/H∞ Robust Control of an Active Magnetic Bearings System
title_full	Intelligent H2/H∞ Robust Control of an Active Magnetic Bearings System
title_fullStr	Intelligent H2/H∞ Robust Control of an Active Magnetic Bearings System
title_full_unstemmed	Intelligent H2/H∞ Robust Control of an Active Magnetic Bearings System
title_sort	intelligent h2/h∞ robust control of an active magnetic bearings system
publisher	Al-Khwarizmi College of Engineering – University of Baghdad
publishDate	2015
url	https://doaj.org/article/1d6caa9bd52948cbb9cf25c9873e0702
work_keys_str_mv	AT safanahmraafat intelligenth2hrobustcontrolofanactivemagneticbearingssystem AT riniakmeliawati intelligenth2hrobustcontrolofanactivemagneticbearingssystem
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Intelligent H2/H∞ Robust Control of an Active Magnetic Bearings System

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