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Networked Iterative Learning Control Design for Nonlinear Systems with Stochastic Output Packet Dropouts
Author(s) -
Liu Jian,
Ruan Xiaoe
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.1457
Subject(s) - iterative learning control , nonlinear system , control theory (sociology) , network packet , compensation (psychology) , computer science , bernoulli's principle , mathematical optimization , control (management) , mathematics , engineering , artificial intelligence , psychology , computer network , physics , quantum mechanics , psychoanalysis , aerospace engineering
This paper develops two proportional‐type (P‐type) networked iterative learning control (NILC) schemes for a class of discrete‐time nonlinear systems whose stochastic output packet dropouts are modeled as 0‐1 Bernoulli stochastic sequences. In constructing the NILC schemes, two kinds of compensation algorithm of the dropped outputs are given. One is to replace the instant‐wise dropped output data with the synchronous desired output data; the other is to substitute the dropped data with the consensus‐instant output data used at the previous iteration. By adopting the lifting technique, it is derived that under certain conditions the expectations of the tracking errors incurred by the proposed NILC schemes converge to zero along the iteration axis. Numerical experiments are carried out for validity and effectiveness.