Online Quantitative feedback theory (QFT)-based self-tuning controller for grain drying process

This paper presents a development of QFT-based self-tuning controller for a conveyor belt type grain dryer plant. Grain drying process is complex due to long time delay, presence of disturbances and plant uncertainty. QFT technique potentially has excellent solution towards this problem due to its w...

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Main Authors:	Mansor, Hasmah, Mohd Noor, Samsul Bahari, Raja Ahmad, Raja Kamil, Taip, Farah Saleena
Format:	Article
Language:	English
Published:	Academic Journals 2011
Subjects:	TA Engineering (General). Civil engineering (General) TK Electrical engineering. Electronics Nuclear engineering
Online Access:	http://irep.iium.edu.my/12334/ http://irep.iium.edu.my/12334/ http://irep.iium.edu.my/12334/ http://irep.iium.edu.my/12334/1/Mansor%2520et%2520al.pdf

id	iium-12334
recordtype	eprints
spelling	iium-123342011-12-23T02:04:24Z http://irep.iium.edu.my/12334/ Online Quantitative feedback theory (QFT)-based self-tuning controller for grain drying process Mansor, Hasmah Mohd Noor, Samsul Bahari Raja Ahmad, Raja Kamil Taip, Farah Saleena TA Engineering (General). Civil engineering (General) TK Electrical engineering. Electronics Nuclear engineering This paper presents a development of QFT-based self-tuning controller for a conveyor belt type grain dryer plant. Grain drying process is complex due to long time delay, presence of disturbances and plant uncertainty. QFT technique potentially has excellent solution towards this problem due to its well known capability to achieve robust performance regardless parameters variation and disturbances. The mathematical model of the grain dryer plant is obtained using system identification based on real-time input/output data. A fixed robust controller could be designed using QFT technique; nevertheless the uncertainty range must be defined. However, in grain drying process, the parameters’ variations are unpredictable and may exceed the defined uncertainty ranges. Therefore, adaptive control with integrated Quantitative Feedback Theory (QFT) constraints is proposed to adapt larger parameters variation. Improved results are obtained by using the proposed method as compared to standard QFT procedure in terms of smaller percentage overshoot and shorter settling time when dealing with larger uncertainty range. In addition, the design methodology of the proposed controller design (loop shaping) was improved such that the dependency on human skills was removed and the controller design was done online. Academic Journals 2011-12-16 Article PeerReviewed application/pdf en http://irep.iium.edu.my/12334/1/Mansor%2520et%2520al.pdf Mansor, Hasmah and Mohd Noor, Samsul Bahari and Raja Ahmad, Raja Kamil and Taip, Farah Saleena (2011) Online Quantitative feedback theory (QFT)-based self-tuning controller for grain drying process. Scientific Research and Essays, 6 (31). pp. 6520-6534. ISSN 1992-2248 http://www.academicjournals.org/SRE/ DOI:10.5897/SRE11.1337
repository_type	Digital Repository
institution_category	Local University
institution	International Islamic University Malaysia
building	IIUM Repository
collection	Online Access
language	English
topic	TA Engineering (General). Civil engineering (General) TK Electrical engineering. Electronics Nuclear engineering
spellingShingle	TA Engineering (General). Civil engineering (General) TK Electrical engineering. Electronics Nuclear engineering Mansor, Hasmah Mohd Noor, Samsul Bahari Raja Ahmad, Raja Kamil Taip, Farah Saleena Online Quantitative feedback theory (QFT)-based self-tuning controller for grain drying process
description	This paper presents a development of QFT-based self-tuning controller for a conveyor belt type grain dryer plant. Grain drying process is complex due to long time delay, presence of disturbances and plant uncertainty. QFT technique potentially has excellent solution towards this problem due to its well known capability to achieve robust performance regardless parameters variation and disturbances. The mathematical model of the grain dryer plant is obtained using system identification based on real-time input/output data. A fixed robust controller could be designed using QFT technique; nevertheless the uncertainty range must be defined. However, in grain drying process, the parameters’ variations are unpredictable and may exceed the defined uncertainty ranges. Therefore, adaptive control with integrated Quantitative Feedback Theory (QFT) constraints is proposed to adapt larger parameters variation. Improved results are obtained by using the proposed method as compared to standard QFT procedure in terms of smaller percentage overshoot and shorter settling time when dealing with larger uncertainty range. In addition, the design methodology of the proposed controller design (loop shaping) was improved such that the dependency on human skills was removed and the controller design was done online.
format	Article
author	Mansor, Hasmah Mohd Noor, Samsul Bahari Raja Ahmad, Raja Kamil Taip, Farah Saleena
author_facet	Mansor, Hasmah Mohd Noor, Samsul Bahari Raja Ahmad, Raja Kamil Taip, Farah Saleena
author_sort	Mansor, Hasmah
title	Online Quantitative feedback theory (QFT)-based self-tuning controller for grain drying process
title_short	Online Quantitative feedback theory (QFT)-based self-tuning controller for grain drying process
title_full	Online Quantitative feedback theory (QFT)-based self-tuning controller for grain drying process
title_fullStr	Online Quantitative feedback theory (QFT)-based self-tuning controller for grain drying process
title_full_unstemmed	Online Quantitative feedback theory (QFT)-based self-tuning controller for grain drying process
title_sort	online quantitative feedback theory (qft)-based self-tuning controller for grain drying process
publisher	Academic Journals
publishDate	2011
url	http://irep.iium.edu.my/12334/ http://irep.iium.edu.my/12334/ http://irep.iium.edu.my/12334/ http://irep.iium.edu.my/12334/1/Mansor%2520et%2520al.pdf
first_indexed	2023-09-18T20:21:33Z
last_indexed	2023-09-18T20:21:33Z
_version_	1777408140314148864

Online Quantitative feedback theory (QFT)-based self-tuning controller for grain drying process

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