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A self‐tuning iterative learning controller for time variant systems
Author(s) -
Madady Ali
Publication year - 2008
Publication title -
asian journal of control
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.769
H-Index - 53
eISSN - 1934-6093
pISSN - 1561-8625
DOI - 10.1002/asjc.67
Subject(s) - iterative learning control , convergence (economics) , control theory (sociology) , controller (irrigation) , computer science , adaptive control , constant (computer programming) , control (management) , lyapunov function , iterative method , mathematical optimization , mathematics , control engineering , artificial intelligence , engineering , nonlinear system , physics , quantum mechanics , agronomy , economics , biology , programming language , economic growth
We consider the iterative learning control problem from an adaptive control viewpoint. The self‐tuning iterative learning control systems (STILCS) problem is formulated in a general case, where the underlying linear system is time‐variant and its parameters are all unknown and where its initial conditions are not constant and not determinable in various iterations. A procedure for solving this problem will be presented. The Lyapunov technique is employed to ensure the convergence of the presented STILCS. Computer simulation results are included to illustrate the effectiveness of the proposed STILCS. Copyright © 2008 John Wiley and Sons Asia Pte Ltd and Chinese Automatic Control Society