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• W4313253286 abstract "This study investigated the gain-adaptation mechanism of decentralized learning control for large-scale interconnected systems subject to measurement noise. The control objective is to minimize asymptotically averaged tracking errors in the iteration domain. The state-coupling matrix concept is employed to model the interactions among subsystems. Decentralized learning control schemes are proposed with three gain sequences: a predefined decreasing gain sequence, global performance-adaptive gain sequence, and decentralized adaptive gain sequence. The input sequences generated by the proposed schemes are shown to be convergent in the mean-square sense. Illustrative simulations are performed to verify the theoretical results." @default.
• W4313253286 created "2023-01-06" @default.
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• W4313253286 date "2023-04-01" @default.
• W4313253286 modified "2023-10-01" @default.
• W4313253286 title "Decentralized learning control for large-scale systems with gain-adaptation mechanisms" @default.
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• W4313253286 doi "https://doi.org/10.1016/j.ins.2022.12.043" @default.
• W4313253286 hasPublicationYear "2023" @default.
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