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<Article>
<Journal>
				<PublisherName>University of Guilan</PublisherName>
				<JournalTitle>Journal of Mathematical Modeling</JournalTitle>
				<Issn>2345-394X</Issn>
				<Volume>14</Volume>
				<Issue>1</Issue>
				<PubDate PubStatus="epublish">
					<Year>2026</Year>
					<Month>03</Month>
					<Day>01</Day>
				</PubDate>
			</Journal>
<ArticleTitle>Dynamic adaptation strategies for optimal control in unknown linear time-invariant system</ArticleTitle>
<VernacularTitle></VernacularTitle>
			<FirstPage>1</FirstPage>
			<LastPage>17</LastPage>
			<ELocationID EIdType="pii">8876</ELocationID>
			
<ELocationID EIdType="doi">10.22124/jmm.2025.30264.2716</ELocationID>
			
			<Language>EN</Language>
<AuthorList>
<Author>
					<FirstName>Homa</FirstName>
					<LastName>Pouyanfar</LastName>
<Affiliation>Department of Applied Mathematics, Faculty of Mathematical Sciences, Ferdowsi University of Mashhad, P. O. Box 1159, Mashhad 91775, Iran</Affiliation>

</Author>
<Author>
					<FirstName>Sohrab</FirstName>
					<LastName>Effati</LastName>
<Affiliation>Department of Applied Mathematics, Faculty of Mathematical Sciences, Ferdowsi University of Mashhad, P. O. Box 1159, Mashhad 91775, Iran</Affiliation>

</Author>
<Author>
					<FirstName>Amin</FirstName>
					<LastName>Mansoori</LastName>
<Affiliation>Department of Applied Mathematics, Faculty of Mathematical Sciences, Ferdowsi University of Mashhad, P. O. Box 1159, Mashhad 91775, Iran</Affiliation>

</Author>
</AuthorList>
				<PublicationType>Journal Article</PublicationType>
			<History>
				<PubDate PubStatus="received">
					<Year>2025</Year>
					<Month>04</Month>
					<Day>04</Day>
				</PubDate>
			</History>
		<Abstract>This paper presents a framework for online adaptive optimal control of continuous-time linear systems with unknown dynamics. The approach uses approximate and adaptive dynamic programming to learn the optimal control policy and value function in real-time, without prior knowledge of the system matrices. We introduce two algorithms based on policy iteration and value iteration, providing proofs the convergence and stability. Our value iteration method is robust against  from  exploration noise. The effectiveness of these control strategies is demonstrated through two examples, highlighting their ability to achieve near-optimal performance despite unknown dynamics. </Abstract>
		<ObjectList>
			<Object Type="keyword">
			<Param Name="value">optimal control</Param>
			</Object>
			<Object Type="keyword">
			<Param Name="value">Adaptive dynamic programming</Param>
			</Object>
			<Object Type="keyword">
			<Param Name="value">Policy iteration</Param>
			</Object>
			<Object Type="keyword">
			<Param Name="value">Value iteration</Param>
			</Object>
			<Object Type="keyword">
			<Param Name="value">Exploration noise</Param>
			</Object>
		</ObjectList>
<ArchiveCopySource DocType="pdf">https://jmm.guilan.ac.ir/article_8876_121fae0e0bfcfd9f477de65a53e5cff7.pdf</ArchiveCopySource>
</Article>
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