Contraction analysis of nonlinear noncausal iterative learning control

被引：11

作者：

Kong, Felix H. ^{[1
,2
]}

Manchester, Ian R. ^{[1
,2
]}

机构：

[1] Univ Sydney, Australian Ctr Field Robot, Sydney, NSW, Australia

[2] Univ Sydney, Sydney Inst Robot & Intelligent Syst, Sch Aerosp Mech & Mechatron Syst, Sydney, NSW, Australia

来源：

SYSTEMS & CONTROL LETTERS | 2020年 / 136卷

基金：

澳大利亚研究理事会;

关键词：

Iterative learning control; Contraction analysis; Linear matrix inequalities; ILC; CRITERIA; METRICS; DESIGN; CONVEX; TIME;

D O I：

10.1016/j.sysconle.2019.104599

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

Iterative learning control (ILC) is a method for learning input signals for repetitive control tasks. In this paper, we provide a new method based on convex optimization for certifying convergence and estimating convergence rate in ILC schemes involving a nonlinear plant and a noncausal update law, which are common in practice. Using sum-of-squares (SOS) optimization, we compute the convergence rate of an example nonlinear, noncausal ILC system and verify its accuracy in experiment. (C) 2019 Elsevier B.V. All rights reserved.

引用

页数：8

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