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Adaptive fuzzy learning control for a class of nonlinear dynamic systems
Author(s) -
Seo Won G.,
Park B. H.,
Lee Jin S.
Publication year - 2000
Publication title -
international journal of intelligent systems
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.291
H-Index - 87
eISSN - 1098-111X
pISSN - 0884-8173
DOI - 10.1002/1098-111x(200012)15:12<1157::aid-int3>3.0.co;2-v
Subject(s) - control theory (sociology) , iterative learning control , fuzzy logic , tracking error , controller (irrigation) , nonlinear system , fuzzy control system , computer science , stability (learning theory) , adaptive control , mathematics , control (management) , artificial intelligence , machine learning , physics , quantum mechanics , agronomy , biology
This paper presents an adaptive iterative learning control scheme that is applicable to a class of nonlinear systems. The control scheme guarantees system stability and boundedness by using the feedback controller coupled with the fuzzy compensator and achieves precise tracking by using the iterative learning rules. In the feedback plus fuzzy compensator unit, the feedback control part stabilizes the overall closed‐loop system and keeps its error bounded, and the fuzzy compensator estimates and compensates for the nonlinear part of the system, thereby keeping the feedback gains reasonably low in the feedback controller. The fuzzy compensator is designed by applying the fuzzy approximation technique to the uncertain nonlinear term to be compensated. In the iterative learning controller, a simple learning control rule is used to achieve precise tracking of the reference signal and a parameter learning algorithm is used to update the parameters in the fuzzy compensator so as to identify the uncertain nonlinearity as much as possible. © 2000 John Wiley & Sons, Inc.