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Machine Intelligence Research

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01-06-2015 | Regular Paper

Discrete-frequency convergence of iterative learning control for linear time-invariant systems with higher-order relative degree

Authors: Xiao-E. Ruan, Zhao-Zhen Li, Z. Z. Bien

Published in: Machine Intelligence Research | Issue 3/2015

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Abstract

In this paper, a discrete-frequency technique is developed for analyzing sufficiency and necessity of monotone convergence of a proportional higher-order-derivative iterative learning control scheme for a class of linear time-invariant systems with higher-order relative degree. The technique composes of two steps. The first step is to expand the iterative control signals, its driven outputs and the relevant signals as complex-form Fourier series and then to deduce the properties of the Fourier coefficients. The second step is to analyze the sufficiency and necessity of monotone convergence of the proposed proportional higher-order-derivative iterative learning control scheme by assessing the tracking errors in the forms of Paserval’s energy modes. Numerical simulations are illustrated to exhibit the validity and the effectiveness.

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Title: Discrete-frequency convergence of iterative learning control for linear time-invariant systems with higher-order relative degree
Authors: Xiao-E. Ruan
Zhao-Zhen Li
Z. Z. Bien
Publication date: 01-06-2015
Publisher: Institute of Automation, Chinese Academy of Sciences
Published in: Machine Intelligence Research / Issue 3/2015
Print ISSN: 2731-538X
Electronic ISSN: 2731-5398
DOI: https://doi.org/10.1007/s11633-015-0884-z

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