L∞ identification and model reduction using a learning genetic algorithm

Kay Chen Tan; Yun Li

L∞ identification and model reduction using a learning genetic algorithm

Kay Chen Tan, Yun Li

Research output: Contribution to journal › Article › peer-review

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.

Original language	English
Pages (from-to)	1125-1130
Number of pages	6
Journal	IEE Conference Publication
Issue number	427 /2
Publication status	Published - 1 Dec 1996
Externally published	Yes

Keywords

genetic algorithms
system identification
model reduction
robust control applications

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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.",

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