Noncausal iterative learning control for uncertain LTI systems

被引：0

作者：

Goldsmith, PB ^{[1
]}

机构：

[1] Univ Calgary, Dept Mech & Mfg Engn, Calgary, AB T2N 1N4, Canada

来源：

PROCEEDINGS OF THE 11TH INTERNATIONAL CONFERENCE ON ADVANCED ROBOTICS 2003, VOL 1-3 | 2003年

关键词：

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

This paper proposes a noncausal Iterative Learning Control (ILC) design that employs the stable inverse of a plant model. Robust performance conditions are derived. The method guarantees convergence to zero error for any LTI plant, including unstable nonminimum-phase plants of any relative degree, as long as certain conditions on plant uncertainty are satisfied. This represents a major improvement over causal ILC, whose performance is limited to that of feedback control.

引用

页码：293 / 298

页数：6

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