L∞ IDENTIFICATION AND MODEL REDUCTION USING A LEARNING GENETIC ALGORITHM

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)22_Publication in policy or professional journalpeer-review

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Author(s)

Detail(s)

Original languageEnglish
Pages (from-to)1125-1130
Journal / PublicationIEE Conference Publication
Issue number427/2
Publication statusPublished - 1996
Externally publishedYes

Conference

TitleUKACC International Conference on (CONTROL'96)
LocationUniversity of Exeter
PlaceUnited Kingdom
CityExeter
Period2 - 5 September 1996

Abstract

This paper develops a Boltzmann learning enhanced genetic algorithm for L norm based system identification and model reduction for robust control applications. Using this technique, both a globally optimised nominal model and an error bounding function for additive and multiplicative uncertainties can be obtained. It can also offer a tighter L error bound and is applicable to both continuous and discrete-time systems.

Research Area(s)

  • System identification, Model reduction, Robust control, Genetic Algorithms