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.
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页数：8
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