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<Article>
<Journal>
				<PublisherName>Payame Noor University (PNU)</PublisherName>
				<JournalTitle>Control and Optimization in Applied Mathematics</JournalTitle>
				<Issn>2383-3130</Issn>
				<Volume></Volume>
				<Issue>Articles in Press</Issue>
				<PubDate PubStatus="epublish">
					<Year>2026</Year>
					<Month>05</Month>
					<Day>27</Day>
				</PubDate>
			</Journal>
<ArticleTitle>Scenario Approach-based Parametric Optimal Control for Uncertain Dynamical Systems via the Variational Iteration Method</ArticleTitle>
<VernacularTitle></VernacularTitle>
			<FirstPage></FirstPage>
			<LastPage></LastPage>
			<ELocationID EIdType="pii">12992</ELocationID>
			
<ELocationID EIdType="doi">10.30473/coam.2026.76729.1376</ELocationID>
			
			<Language>EN</Language>
<AuthorList>
<Author>
					<FirstName>Ghania</FirstName>
					<LastName>Idiri</LastName>
<Affiliation>Laboratoire de Conception et Conduite des Systèmes de Production, Université Mouloud Mammeri, 15 000 Tizi-Ouzou, Algérie</Affiliation>

</Author>
</AuthorList>
				<PublicationType>Journal Article</PublicationType>
			<History>
				<PubDate PubStatus="received">
					<Year>2025</Year>
					<Month>12</Month>
					<Day>09</Day>
				</PubDate>
			</History>
		<Abstract>In this paper, a systematic design approach for the parametric optimization of uncertain dynamical systems with bounded parameter uncertainties is proposed. The methodology proceeds in two stages. In the first stage, the control input is expressed as a finite linear combination of polynomial basis functions, and an approximate analytical solution of the state equation is derived using the Variational Iteration Method, which is a semi-analytical iterative technique that avoids discretization and linearization. Substituting this approximate trajectory into the performance index yields a robust min–max optimization problem parameterized by the control coefficients. In the second stage, the robust optimization problem is converted into a tractable scenario optimization problem by drawing a finite number of independent and identically distributed samples from the uncertainty set. The resulting problem is solved using a genetic algorithm. The effectiveness of the proposed approach is demonstrated through two application examples. The first example concerns an uncertain linear-quadratic regulator, and the second addresses an uncertain nonlinear optimal control problem. Optimal control and state trajectories are provided for a set of samples. In addition, the optimal value of the performance index is reported, showing that this value does not exceed the threshold imposed by the proposed approach. The paper concludes by discussing limitations, including dependence on the accuracy of the Variational Iteration Method approximation and the assumption of a known probability distribution over the uncertainty set, and identifies directions for future research.</Abstract>
		<ObjectList>
			<Object Type="keyword">
			<Param Name="value">Optimal Control</Param>
			</Object>
			<Object Type="keyword">
			<Param Name="value">Parametric optimization</Param>
			</Object>
			<Object Type="keyword">
			<Param Name="value">Uncertain system</Param>
			</Object>
			<Object Type="keyword">
			<Param Name="value">Variational iteration method</Param>
			</Object>
			<Object Type="keyword">
			<Param Name="value">Scenario approach</Param>
			</Object>
		</ObjectList>
<ArchiveCopySource DocType="pdf">https://mathco.journals.pnu.ac.ir/article_12992_3942c1f2fe17b0555c70a4426258a885.pdf</ArchiveCopySource>
</Article>
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