Open AccessIterative learning control for semi‐linear distributed parameter systems based on sensor–actuator networks
Author(s) -
Zhang Jianxiang,
Cui Baotong,
Lou Xu Yang
Publication year - 2020
Publication title -
iet control theory and applications
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.059
H-Index - 108
eISSN - 1751-8652
pISSN - 1751-8644
DOI - 10.1049/iet-cta.2019.0315
Subject(s) - control theory (sociology) , iterative learning control , bounded function , noise (video) , actuator , lemma (botany) , mathematics , tracking (education) , tracking error , distributed parameter system , controller (irrigation) , computer science , partial differential equation , control (management) , artificial intelligence , mathematical analysis , psychology , ecology , pedagogy , poaceae , agronomy , image (mathematics) , biology
In this study, the iterative learning control (ILC) method is considered for tracking control of a class of distributed parameter systems (DPSs) based on sensor–actuator networks (SANs) with the unknown exogenous input and the measurement noise, which are described by a semi‐linear parabolic partial differential equation. The D‐type ILC algorithm is presented to control DPSs with non‐collocated SANs. When the unknown exogenous input and the measurement noise are bounded, the upper bounds of output errors are obtained via the Bellman–Gronwall lemma and semi‐group theory, respectively. The authors prove that the output errors converge to zero in the absence of the unknown exogenous input and the measurement noise. Two examples are given to show the effectiveness of the proposed D‐type ILC scheme.