On almost sure and mean square convergence of P-type ILC under randomly varying iteration lengths

This note proposes convergence analysis of iterative learning control (ILC) for discrete-time linear systems with randomly varying iteration lengths. No prior information is required on the probability distribution of randomly varying iteration lengths. The conventional P-type update law is adopted...

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Bibliographic Details
Published inAutomatica (Oxford) Vol. 63; pp. 359 - 365
Main Authors Shen, Dong, Zhang, Wei, Wang, Youqing, Chien, Chiang-Ju
Format Journal Article
LanguageEnglish
Published Elsevier Ltd 01.01.2016
Subjects
Almost sure convergence
Iterative learning control
Mean square convergence
Non-uniform iteration length
Mean square convergence
Almost sure convergence
Non-uniform iteration length
Iterative learning control
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Summary:This note proposes convergence analysis of iterative learning control (ILC) for discrete-time linear systems with randomly varying iteration lengths. No prior information is required on the probability distribution of randomly varying iteration lengths. The conventional P-type update law is adopted with Arimoto-like gain and/or causal gain. The convergence both in almost sure and mean square senses is proved by direct math calculating. Numerical simulations verifies the theoretical analysis.
ISSN:0005-1098
1873-2836
DOI:10.1016/j.automatica.2015.10.050