An iterative learning algorithm founded on the normalization principle and inverse triangular dynamic initial error compensation
Transactions of the Institute of Measurement and Control
Published online on August 09, 2026
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 ...
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 ...