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Multi‐parametric extremum seeking‐based iterative feedback gains tuning for nonlinear control
Author(s) -
Benosman Mouhacine
Publication year - 2016
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.3547
Subject(s) - control theory (sociology) , feedback linearization , parametric statistics , nonlinear system , controller (irrigation) , tracking error , robust control , bounded function , linearization , iterative learning control , computer science , convergence (economics) , nonlinear control , stability (learning theory) , mathematics , mathematical optimization , control (management) , artificial intelligence , mathematical analysis , statistics , physics , quantum mechanics , machine learning , agronomy , economics , biology , economic growth
Summary We study in this paper the problem of iterative feedback gains auto‐tuning for a class of nonlinear systems. For the class of input–output linearizable nonlinear systems with bounded additive uncertainties, we first design a nominal input–output linearization‐based robust controller that ensures global uniform boundedness of the output tracking error dynamics. Then, we complement the robust controller with a model‐free multi‐parametric extremum seeking control to iteratively auto‐tune the feedback gains. We analyze the stability of the whole controller, that is, the robust nonlinear controller combined with the multi‐parametric extremum seeking model‐free learning algorithm. We use numerical tests to demonstrate the performance of this method on a mechatronics example. Copyright © 2016 John Wiley & Sons, Ltd.