LMI Approach to Robust Monotonically Convergent Iterative Learning Control for Uncertain Linear Discrete-time Systems

被引：0

作者：

Li Zhifu ^{[1
]}

Yuan Peng ^{[1
]}

Hu Yueming ^{[1
]}

Guo Qiwei ^{[1
]}

Ma Ge ^{[1
]}

机构：

[1] South China Univ Technol, Engn Res Ctr Precis Elect Mfg Equipments, Minist Educ, Coll Automat Sci & Engn, Guangzhou 510640, Guangdong, Peoples R China

来源：

PROCEEDINGS OF THE 31ST CHINESE CONTROL CONFERENCE | 2012年

关键词：

Iterative learning control; uncertain linear systems; quadratic performance; robust control; linear matrix inequality;

D O I：

暂无

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

This paper presents a robust monotonically convergent (RMC) iterative learning control (ILC) design for a class of uncertain linear discrete-time systems with non-zero constant initial error. The learning law under consideration is an anticipatory ILC. Based on a simple quadratic performance function, a sufficient condition for robust monotonic convergence of the proposed learning algorithm is presented in terms of linear matrix inequality (LMI). Finally, a simulation example is given to show the effectiveness of the proposed scheme.

引用

页码：2889 / 2894

页数：6

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