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Pseudo‐downsampled iterative learning control
Author(s) -
Zhang Bin,
Wang Danwei,
Zhou Keliang,
Ye Yongqiang,
Wang Yigang
Publication year - 2007
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.1232
Subject(s) - iterative learning control , tracking error , upsampling , control theory (sociology) , convergence (economics) , computer science , computation , tracking (education) , filter (signal processing) , simple (philosophy) , scheme (mathematics) , reduction (mathematics) , control (management) , algorithm , mathematics , artificial intelligence , psychology , mathematical analysis , pedagogy , philosophy , geometry , epistemology , economics , image (mathematics) , computer vision , economic growth
In this paper, a simple and effective multirate iterative learning control (ILC), referred as pseudo‐downsampled ILC, is proposed to deal with initial state error. This scheme downsamples the tracking error and input signals collected from the feedback control system before they are used in the ILC learning law. The output of the ILC is interpolated to generate the input for the next cycle. Analysis shows that the exponential decay of the tracking error can be expected and convergence condition can be ensured by downsampling. Other advantages of the proposed pseudo‐downsampled ILC include no need for a filter design and reduction of memory size and computation. Experimental results demonstrate the effectiveness of the proposed scheme. Copyright © 2007 John Wiley & Sons, Ltd.