Open AccessIterative learning control for discrete‐time systems with quantised measurements
Author(s) -
Xuhui Bu,
Taihua Wang,
Zhongsheng Hou,
Ronghu Chi
Publication year - 2015
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.2014.1056
Subject(s) - iterative learning control , convergence (economics) , control theory (sociology) , logarithm , tracking error , linear system , tracking (education) , mathematics , extension (predicate logic) , repetitive control , scheme (mathematics) , iterative method , computer science , control system , control (management) , algorithm , artificial intelligence , mathematical analysis , engineering , psychology , pedagogy , electrical engineering , economics , programming language , economic growth
In this study, the problem of iterative learning control (ILC) for discrete‐time systems with quantised output measurements is considered. Here, a logarithmic quantiser is introduced and an ILC scheme is constructed by using output signals with only a finite number of quantisation levels. By using sector bound method to deal with the quantisation error, a learning condition of ILC that guarantees the convergence of tracking error is derived through rigorous analysis. It is shown that the convergence condition is determined by quantisation level, and the tracking error converges to a bound depending on quantisation density. Furthermore, the extension from linear systems to non‐linear systems is also addressed. Finally, two illustrative examples are presented to demonstrate the theoretical results for both linear and non‐linear systems.