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Iterative learning control design with high‐order internal model for discrete‐time nonlinear systems
Author(s) -
Zhou Wei,
Yu Miao,
Liu Baobin
Publication year - 2016
Publication title -
international journal of robust and nonlinear control
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.361
H-Index - 106
eISSN - 1099-1239
pISSN - 1049-8923
DOI - 10.1002/rnc.3732
Subject(s) - iterative learning control , control theory (sociology) , integrator , nonlinear system , computer science , discrete time and continuous time , internal model , trajectory , tracking (education) , tracking error , scheme (mathematics) , iterative method , control (management) , mathematical optimization , mathematics , algorithm , artificial intelligence , physics , quantum mechanics , psychology , computer network , pedagogy , mathematical analysis , statistics , bandwidth (computing) , astronomy
Summary In this paper, a high‐order internal model (HOIM)‐based iterative learning control (ILC) scheme is proposed for discrete‐time nonlinear systems to tackle the tracking problem under iteration‐varying desired trajectories. By incorporating the HOIM that is utilized to describe the variation of desired trajectories in the iteration domain into the ILC design, it is shown that the system output can converge to the desired trajectory along the iteration axis within arbitrarily small error. Furthermore, the learning property in the presence of state disturbances and output noise is discussed under HOIM‐based ILC with an integrator in the iteration axis. Two simulation examples are given to demonstrate the effectiveness of the proposed control method. Copyright © 2016 John Wiley & Sons, Ltd.