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Iterative learning control for uncertain nonlinear networked control systems with random packet dropout
Author(s) -
Zhang Yamiao,
Liu Jian,
Ruan Xiaoe
Publication year - 2019
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.4568
Subject(s) - iterative learning control , control theory (sociology) , realizability , nonlinear system , bernoulli's principle , computer science , network packet , dropout (neural networks) , scheme (mathematics) , bernoulli distribution , random variable , mathematics , control (management) , algorithm , artificial intelligence , engineering , machine learning , computer network , mathematical analysis , statistics , physics , quantum mechanics , aerospace engineering
Summary This paper proposes a novel networked iterative learning control (NILC) scheme with adjustment factor for a class of discrete‐time uncertain nonlinear systems with stochastic input and output packet dropout modeled as 0‐1 Bernoulli‐type random variable. Firstly, the equivalence relation between the realizability of controlled system and the input‐output coupling parameter (IOCP) is established. Secondly, in order to overcome the main obstacle arising from the unknown IOCP, an identification technique is developed for it. Thirdly, it is strictly proved that, under certain conditions, the tracking errors driven by the developed NILC scheme are convergent to zero along iteration direction in the sense of expectation. Finally, an example is given to demonstrate the effectiveness of the proposed NILC scheme and the merits of adjustment factor.