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- W2892377830 abstract "In this paper, we introduce an adaptive iterative learning control (AILC) law based on high-order internal model (HOIM) for a class of nonlinear multiple input multiple output (MIMO) systems with different non-repetitiveness. Parametric uncertainties vary in HOIMs with unknown initial values and known coefficients. By using state-space modeling, unknown initial values of HOIMs are extracted. Furthermore, unknown matrix of all of the uncertainties is obtained which means less calculation in algorithm. The proposed AILC scheme can also remove the influence of randomly iteration-varying initial states and iteratively varying reference trajectories. Strict theoretical proof and illustrative example demonstrate the performance and effectiveness of the proposed AILC law." @default.
- W2892377830 created "2018-09-27" @default.
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- W2892377830 date "2018-08-01" @default.
- W2892377830 modified "2023-09-24" @default.
- W2892377830 title "Adaptive ILC Approach for Nonlinear MIMO Systems with Parametric Uncertainties" @default.
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- W2892377830 doi "https://doi.org/10.1109/iccse.2018.8468747" @default.
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