Open AccessRobust higher‐order ILC for non‐linear discrete‐time systems with varying trail lengths and random initial state shifts
Author(s) -
Wei YunShan,
Li XiaoDong
Publication year - 2017
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.2017.0008
Subject(s) - iterative learning control , control theory (sociology) , bounded function , mathematics , tracking (education) , state (computer science) , linear system , discrete time and continuous time , computer science , algorithm , control (management) , mathematical analysis , statistics , artificial intelligence , psychology , pedagogy
This study addresses a robust iterative learning control (ILC) scheme for non‐linear discrete‐time systems in which both the trail lengths and the initial state shifts could be randomly variant in iteration domain. The proposed higher‐order ILC law guarantees that as the iteration number goes to infinity, the ILC tracking errors at the desired output trail period are bounded in mathematical expectation, and the bound of tracking errors is proportional to the random initial state shifts. Specifically, the ILC tracking errors in mathematical expectation can be driven to zero as the expectation of initial state shifts is zero. Two numerical examples are carried out to demonstrate the effectiveness of the proposed higher‐order ILC law.