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• W4226076547 abstract "AbstractIn Chaps. 2–8, many DAILC methods have been presented for discrete-time systems such that one can select a proper method for the specific applications. For example, if the realistic plant can be modeled by a parametric system, the DAILC presented in Chaps. 2 and 3 can be applied. If the real plant contains some hard nonlinearities, the DAILC methods-based nonlinearity estimator or neural networks presented in Chaps. 4 and 5 may be a proper selection. For a multi-agent system, the presented distributed DAILC method in Chap. 6 is suitable since it uses the consensus error in the learning control algorithm. Further, for a practical plant that is too complex to obtain the exact mechanistic model, the data-driven DAILC methods presented in Chaps. 7 and 8 are the suitable choices." @default.
• W4226076547 created "2022-05-05" @default.
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• W4226076547 date "2022-01-01" @default.
• W4226076547 modified "2023-10-17" @default.
• W4226076547 title "Data-Driven Discrete-Time Adaptive ILC for Terminal Tracking" @default.
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• W4226076547 doi "https://doi.org/10.1007/978-981-19-0464-6_9" @default.
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