Premium
Robust iterative learning control for systems with norm‐bounded uncertainties
Author(s) -
Li Xuefang,
Huang Deqing,
Chu Bing,
Xu JianXin
Publication year - 2015
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.3333
Subject(s) - iterative learning control , lipschitz continuity , control theory (sociology) , bounded function , norm (philosophy) , nonlinear system , robust control , computer science , tracking error , scheme (mathematics) , mathematical optimization , mathematics , control (management) , artificial intelligence , mathematical analysis , physics , political science , law , quantum mechanics
Summary A new robust iterative learning control scheme is presented for state tracking control of nonlinear MIMO systems. The main characteristic of the proposed controller lies in its ability to deal with unstructured uncertainties that are norm‐bounded but not globally or locally Lipschitz continuous as usual. The classical resetting condition of iterative learning control is removed and replaced with more practical alignment condition. The class of systems to be considered is further extended to more general scenarios, in which input distribution uncertainties are included. In the end, an illustrative example is presented to demonstrate the efficacy of the proposed control scheme. Copyright © 2015 John Wiley & Sons, Ltd.