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Iterative Learning Control for Nonlinear Systems with Data Dropouts at Both Measurement and Actuator Sides
Author(s) -
Jin Yanqiong,
Shen Dong
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.1656
Subject(s) - iterative learning control , control theory (sociology) , actuator , nonlinear system , signal (programming language) , computer science , controller (irrigation) , iterative method , control (management) , control system , control engineering , mathematics , algorithm , engineering , artificial intelligence , physics , electrical engineering , quantum mechanics , agronomy , biology , programming language
This paper discusses the iterative learning control (ILC) for nonlinear systems under a general networked control structure, in which random data dropouts occur independently at both measurement and actuator sides. Both updating algorithms are proposed for the computed input signal at the learning controller and the real input signal at the plant, respectively. The system output is strictly proved to converge to the desired reference with probability one as the iteration number goes to infinity. A numerical simulation is provided to verify the effectiveness of the proposed mechanism and algorithms.