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.
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