On fuzzy alternate control systems

Onasanya, BO; Feng, Y; Zhang, W; Wen, S; Tang, N

On fuzzy alternate control systems

Onasanya, BO Feng, Y Zhang, W Wen, S

Tang, N

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Publisher:: IEEE
Publication Type:: Conference Proceeding
Citation:: 2021 13th International Conference on Advanced Computational Intelligence (ICACI), 2021, 00, pp. 172-178
Issue Date:: 2021

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Field	Value	Language
dc.contributor.author	Onasanya, BO
dc.contributor.author	Feng, Y
dc.contributor.author	Zhang, W
dc.contributor.author	Wen, S https://orcid.org/0000-0001-8077-7001
dc.contributor.author	Tang, N
dc.date	2021-05-14
dc.date.accessioned	2022-06-14T04:50:57Z
dc.date.available	2022-06-14T04:50:57Z
dc.date.issued	2021
dc.identifier.citation	2021 13th International Conference on Advanced Computational Intelligence (ICACI), 2021, 00, pp. 172-178
dc.identifier.isbn	978-1-6654-1254-4
dc.identifier.issn	2573-3311
dc.identifier.uri	http://hdl.handle.net/10453/158159
dc.description.abstract	Some researchers have worked on intermittent control and alternate control. In both cases, the choice of the control was a constant quantity. But, in real life, this situation is hard to come by for the reason that control may vary with time and circumstances owing to machine or human errors or both. Hence is the need to employ a method in which the control matrix is rather uncertain as is obtainable in real life. In this paper, we consider a more general model of both intermittent and alternate control which allows the control matrix to be fuzzy (uncertain). It turns out that both the classical intermittent and classical alternate controls are recovered in this mode.
dc.language	en
dc.publisher	IEEE
dc.relation.ispartof	2021 13th International Conference on Advanced Computational Intelligence (ICACI)
dc.relation.ispartof	International Conference on Advanced Computational Intelligence
dc.relation.isbasedon	10.1109/icaci52617.2021.9435911
dc.rights	info:eu-repo/semantics/closedAccess
dc.title	On fuzzy alternate control systems
dc.type	Conference Proceeding
utslib.citation.volume	00
utslib.location.activity	Wanzhou, China
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Strength - AAII - Australian Artificial Intelligence Institute
utslib.copyright.status	closed_access	*
pubs.consider-herdc	false
dc.date.updated	2022-06-14T04:50:56Z
pubs.finish-date	2021-05-16
pubs.place-of-publication	Piscataway, USA
pubs.publication-status	Published
pubs.start-date	2021-05-14
pubs.volume	00
dc.location	Piscataway, USA

Abstract:

Some researchers have worked on intermittent control and alternate control. In both cases, the choice of the control was a constant quantity. But, in real life, this situation is hard to come by for the reason that control may vary with time and circumstances owing to machine or human errors or both. Hence is the need to employ a method in which the control matrix is rather uncertain as is obtainable in real life. In this paper, we consider a more general model of both intermittent and alternate control which allows the control matrix to be fuzzy (uncertain). It turns out that both the classical intermittent and classical alternate controls are recovered in this mode.

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http://hdl.handle.net/10453/158159