Initial Input Selection for Iterative Learning Control

被引：8

作者：

Freeman, Chris T. ^{[1
]}

Alsubaie, Muhammad Ali ^{[1
]}

Cai, Zhonglun ^{[1
]}

Rogers, Eric ^{[1
]}

Lewin, Paul L. ^{[1
]}

机构：

[1] Univ Southampton, Sch Elect & Comp Sci, Southampton SO17 1BJ, Hants, England

来源：

JOURNAL OF DYNAMIC SYSTEMS MEASUREMENT AND CONTROL-TRANSACTIONS OF THE ASME | 2011年 / 133卷 / 05期

关键词：

REPETITIVE CONTROL; EXPERIENCE; TIME;

D O I：

10.1115/1.4003096

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 algorithms have been shown to offer a high level of performance both theoretically and when applied to practical applications. However, the trial-to-trial convergence of the error is generally highly dependent on the initial choice of input applied to the plant. Techniques are therefore developed, which generate an optimal initial input selection, and the effect this has on the error over subsequent trials is examined. Experimental benchmarking is undertaken using a gantry robot test facility. [DOI: 10.1115/1.4003096]

引用

页数：6

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