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Adaptive iterative learning control of discrete‐time varying systems with unknown control direction
Author(s) -
Yan Weili,
Sun Mingxuan
Publication year - 2013
Publication title -
international journal of adaptive control and signal processing
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.73
H-Index - 66
eISSN - 1099-1115
pISSN - 0890-6327
DOI - 10.1002/acs.2312
Subject(s) - iterative learning control , control theory (sociology) , adaptive control , bounded function , convergence (economics) , discrete time and continuous time , computer science , scheme (mathematics) , interval (graph theory) , control (management) , sign (mathematics) , tracking (education) , property (philosophy) , iterative method , mathematical optimization , mathematics , algorithm , artificial intelligence , psychology , mathematical analysis , pedagogy , statistics , combinatorics , economics , economic growth , philosophy , epistemology
SUMMARY Without using Nussbaum gain, a novel method is presented to solve the unknown control direction problem for discrete‐time systems. The underlying idea is to fully exploit the convergence property of parameter estimates in well‐known adaptive algorithms. By incorporating two modifications into the control and the parameter update laws, respectively, we present an adaptive iterative learning control scheme for discrete‐time varying systems without the prior knowledge of the sign of control gain. It is shown that the proposed adaptive iterative learning control can achieve perfect tracking over the finite time interval while all the closed‐loop signals remain bounded. An illustrative example is presented to verify effectiveness of the proposed scheme. Copyright © 2012 John Wiley & Sons, Ltd.