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Quantized Iterative Learning Control Design For Linear Systems Based On A 2‐D Roesser Model
Author(s) -
Yanling Yin,
Xuhui Bu,
Jiaqi Liang
Publication year - 2018
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.1368
Subject(s) - iterative learning control , control theory (sociology) , quantization (signal processing) , logarithm , mathematics , iterative method , linear system , controller (irrigation) , exponential stability , stability (learning theory) , computer science , mathematical optimization , control (management) , algorithm , nonlinear system , artificial intelligence , mathematical analysis , physics , quantum mechanics , machine learning , agronomy , biology
Abstract This paper considers the problem of iterative learning control design for linear systems with data quantization. It is assumed that the control input update signals are quantized before they are transmitted to the iterative learning controller. A logarithmic quantizer is used to decode the signal with a number of quantization levels. Then, a 2‐D Roesser model is established to describe the entire dynamics of the iterative learning control (ILC) system. By using the sector bound method, a sufficient asymptotic stability condition for such a 2‐D system is established and then the ILC design is given simultaneously. The result is also extended to more general cases where the system matrices contain uncertain parameters. The effectiveness of the proposed method is illustrated by a numerical example.