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		<identifier>oai:zbc.uz.zgora.pl:94078</identifier>
	    <datestamp>2026-03-27T09:56:21Z</datestamp>
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<dc:title xml:lang="pl"><![CDATA[Data-enabled iterative learning control: A zero-sum game design for time-scale-varying tasks]]></dc:title>
<dc:creator><![CDATA[Zhuang, Zhihe]]></dc:creator>
<dc:creator><![CDATA[González, Rodrigo A.]]></dc:creator>
<dc:creator><![CDATA[Tao, Hongfeng]]></dc:creator>
<dc:creator><![CDATA[Paszke, Wojciech (1975- )]]></dc:creator>
<dc:creator><![CDATA[Oomen, Tom]]></dc:creator>
<dc:subject xml:lang="pl"><![CDATA[iterative learning control]]></dc:subject>
<dc:subject xml:lang="pl"><![CDATA[time-scale-varying task]]></dc:subject>
<dc:subject xml:lang="pl"><![CDATA[data-based control]]></dc:subject>
<dc:subject xml:lang="pl"><![CDATA[reinforcement learning]]></dc:subject>
<dc:description xml:lang="pl"><![CDATA[Iterative learning control (ILC) is an intelligent control methodology for tackling iteration-invariant exogenous inputs. It is of great significance to develop its extrapolation for more general repetitive tasks with mutual similarity, e.g., tasks with different time scales. In practice, discrete-time ILC with sampling behavior for time-scale-varying tasks suffers from the failure of perfect corresponding learning and environment-dependent iteration-varying disturbances.]]></dc:description>
<dc:description xml:lang="pl"><![CDATA[This paper develops a novel direct databased ILC algorithm using off-policy Q-learning for tasks with varying time scales, enabling the robust learning of an optimal ILC policy from experimental input/output (I/O) data. From a two-player zero-sum game perspective, the iteration-varying disturbance generated from the varying time scales of repetitive tasks is tackled quantitatively with a preset disturbance attenuation level.]]></dc:description>
<dc:description xml:lang="pl"><![CDATA[Further, to emphasize the importance of theoretical guarantees of reinforcement learning (RL)-based ILC designs, the data efficiency of the developed algorithm is enhanced based on Willems` Fundamental Lemma, and a rigorous convergence analysis is given. The simulation model of an F-16 aircraft autopilot is employed to show the effectiveness of the developed approach.]]></dc:description>
<dc:description xml:lang="pl"><![CDATA[artykuł zamieszczony w: "Automatica", Vol. 185]]></dc:description>
<dc:date><![CDATA[2026]]></dc:date>
<dc:type xml:lang="pl"><![CDATA[artykuł]]></dc:type>
<dc:format xml:lang="pl"><![CDATA[application/pdf]]></dc:format>
<dc:identifier><![CDATA[http://www.zbc.uz.zgora.pl/Content/94078/automatica.pdf]]></dc:identifier>
<dc:identifier><![CDATA[https://zbc.uz.zgora.pl/dlibra/publication/105890/edition/94078/content]]></dc:identifier>
<dc:identifier><![CDATA[oai:zbc.uz.zgora.pl:94078]]></dc:identifier>
<dc:language><![CDATA[eng]]></dc:language>
<dc:relation><![CDATA[oai:zbc.uz.zgora.pl:publication:105890]]></dc:relation>
<dc:rights xml:lang="pl"><![CDATA[Biblioteka Uniwersytetu Zielonogórskiego]]></dc:rights>
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