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<!DOCTYPE ArticleSet PUBLIC "-//NLM//DTD PubMed 2.7//EN" "https://dtd.nlm.nih.gov/ncbi/pubmed/in/PubMed.dtd">
<ArticleSet>
<Article>
<Journal>
				<PublisherName>University of Mohaghegh Ardabili</PublisherName>
				<JournalTitle>Journal of Hyperstructures</JournalTitle>
				<Issn>2251-8436</Issn>
				<Volume>12</Volume>
				<Issue>2</Issue>
				<PubDate PubStatus="epublish">
					<Year>2023</Year>
					<Month>12</Month>
					<Day>01</Day>
				</PubDate>
			</Journal>
<ArticleTitle>Convergence analysis of proportional-derivative -type ILC for linear continuous constant time delay switched systems with observation noise and state uncertainties</ArticleTitle>
<VernacularTitle></VernacularTitle>
			<FirstPage>351</FirstPage>
			<LastPage>365</LastPage>
			<ELocationID EIdType="pii">2808</ELocationID>
			
<ELocationID EIdType="doi">10.22098/jhs.2023.2808</ELocationID>
			
			<Language>EN</Language>
<AuthorList>
<Author>
					<FirstName>Omprakash</FirstName>
					<LastName>Dewangan</LastName>
<Affiliation>Indira Gandhi Govt. College Pandaria, Distt.- Kabirdham, Hemchand Yadav Vishwavidyalaya Durg, Chhattisgarh, India</Affiliation>

</Author>
</AuthorList>
				<PublicationType>Journal Article</PublicationType>
			<History>
				<PubDate PubStatus="received">
					<Year>2023</Year>
					<Month>10</Month>
					<Day>26</Day>
				</PubDate>
			</History>
		<Abstract>This article is concerned with the linear continuous time delay switching system with state uncertainties and observa-tion noise. The goal of this study is to investigate how an internal switching mechanism and the efficacy of a conventional proportional-derivative ILC method is impacted by ambient noise for linear continuous-time switching systems. The findings demonstrate that learning gains and the dynamics of the subsystems, rather than the time-driven switching rule, are primarily responsible for the con-vergence and robustness of the control method.An appropriate selection of learning gains can ensure the control algorithm’s con-vergence and resilience given any arbitrary time-varying switching rule.</Abstract>
		<ObjectList>
			<Object Type="keyword">
			<Param Name="value">Iterative learning control</Param>
			</Object>
			<Object Type="keyword">
			<Param Name="value">switched system</Param>
			</Object>
			<Object Type="keyword">
			<Param Name="value">Dynamical system</Param>
			</Object>
			<Object Type="keyword">
			<Param Name="value">time delay</Param>
			</Object>
			<Object Type="keyword">
			<Param Name="value">bounded state disturbance</Param>
			</Object>
			<Object Type="keyword">
			<Param Name="value">bounded observation noise</Param>
			</Object>
		</ObjectList>
<ArchiveCopySource DocType="pdf">https://jhs.uma.ac.ir/article_2808_ba5e3fc6ecb76574c1a247e2959f46b9.pdf</ArchiveCopySource>
</Article>
</ArticleSet>
