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An iterative learning algorithm founded on the normalization principle and inverse triangular dynamic initial error compensation

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Transactions of the Institute of Measurement and Control

Published online on

Abstract

Transactions of the Institute of Measurement and Control, Ahead of Print.
To address the issues of low convergence accuracy and deteriorated output performance caused by initial state errors in linear time-invariant discrete-time systems, traditional iterative learning control methods typically treat initial errors as passively ...