Iterative Learning Control for uncertain systems: Robust monotonic convergence analysis

被引：123

作者：

van de Wijdeven, Jeroen ^{[1
]}

Donkers, Tijs ^{[1
]}

Bosgra, Okko ^{[1
]}

机构：

[1] Eindhoven Univ Technol, Dept Mech Engn, NL-5600 MB Eindhoven, Netherlands

来源：

AUTOMATICA | 2009年 / 45卷 / 10期

关键词：

Iterative Learning Control; Robust stability; Uncertain dynamic systems; STABILITY; DESIGN; TIME; FEEDBACK;

D O I：

10.1016/j.automatica.2009.06.033

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

080201 [机械制造及其自动化];

摘要：

In this paper, we present a novel Robust Monotonic Convergence (RMC) analysis approach for finite time interval Iterative Learning Control (ILC) for uncertain systems. For that purpose, a finite time interval model for uncertain systems is introduced. This model is subsequently used in an RMC analysis based on mu analysis. As a result, we can handle additive and multiplicative uncertainty models in the RMC problem formulation, analyze RMC of linear time invariant MIMO systems controlled by any linear trial invariant ILC controller, and formulate additional straightforward RMC conditions for ILC controlled systems. To illustrate the derived results, we analyze the RMC properties of linear quadratic (LQ) norm optimal ILC. (C) 2009 Elsevier Ltd. All rights reserved.

引用

页码：2383 / 2391

页数：9

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