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Robust Convergence of High-Order Adaptive Iterative Learning Control Against Iteration-Varying Uncertainties
Ist Teil von
Proceedings of 2019 Chinese Intelligent Systems Conference, 2019, Vol.592, p.591-598
Ort / Verlag
Singapore: Springer Singapore Pte. Limited
Erscheinungsjahr
2019
Quelle
Alma/SFX Local Collection
Beschreibungen/Notizen
In this paper, adaptive iterative learning control (AILC) algorithms using a high-order law for linear time-varying (LTV) systems with iteration-varying uncertainties are proposed. Sufficient conditions are derived to guarantee the robust convergence of tracking error such that the bounded-input-bounded-output stability of LTV systems can be achieved. Extensions of established AILC results to nonlinear systems are further developed. Numerical simulations are implemented to validate the effectiveness of the theoretical results.