An Iterative Learning Control Approach for Linear Systems With Randomly Varying Trial Lengths

被引：179

作者：

Li, Xuefang ^{[1
]}

Xu, Jian-Xin ^{[1
]}

Huang, Deqing ^{[2
]}

机构：

[1] Natl Univ Singapore, Dept Elect & Comp Engn, Singapore 117576, Singapore

[2] Univ London Imperial Coll Sci Technol & Med, Dept Aeronaut, London SW7 2AZ, England

来源：

IEEE TRANSACTIONS ON AUTOMATIC CONTROL | 2014年 / 59卷 / 07期

关键词：

Average operator; identical initial condition; iterative learning control (ILC); non-uniform trial length; REPETITIVE CONTROL;

D O I：

10.1109/TAC.2013.2294827

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 technical note addresses an iterative learning control (ILC) design problem for discrete-time linear systems where the trial lengths could be randomly varying in the iteration domain. An ILC scheme with an iteration-average operator is introduced for tracking tasks with non-uniform trial lengths, which thus mitigates the requirement on classic ILC that all trial lengths must be identical. In addition, the identical initialization condition can be absolutely removed. The learning convergence condition of ILC in mathematical expectation is derived through rigorous analysis. As a result, the proposed ILC scheme is applicable to more practical systems. In the end, two illustrative examples are presented to demonstrate the performance and the effectiveness of the averaging ILC scheme for both time-invariant and time-varying linear systems.

引用

页码：1954 / 1960

页数：7

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