Discrete-time model reduction of sampled systems using an enhanced multiresolutional dynamic genetic algorithm

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Abstract

A framework to automatically generate a reduced-order discrete-time model for the sampled system of a continuous plant preceded by a zero-order hold using an enhanced multiresolutional dynamic genetic algorithms (EMDGA) is proposed in this paper. Chromosomes consisting of the denominator and the numerator parameters of the reduced-order model are coded as a vector with floating point type components and searched by the genetic algorithm. Therefore, a stable optimal reduced-order model satisfying the error range specified can be evolutionarily obtained. Because of the use of the multiresolutional dynamic adaptation algorithm and genetic operators, the convergence rate of the evolution process to search for an optimal reduced-order model can be expedited. Another advantage of this approach is that the reduced discrete-time model evolves based on samples directly taken from the continuous plant, instead of the exact discrete-time model, so that computation time is saved.

Original languageEnglish
Pages (from-to)280-285
Number of pages6
JournalProceedings of the IEEE International Conference on Systems, Man and Cybernetics
Volume1
DOIs
Publication statusPublished - 2001 Jan 1

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Genetic algorithms
Chromosomes
Mathematical operators

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Hardware and Architecture

Cite this

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abstract = "A framework to automatically generate a reduced-order discrete-time model for the sampled system of a continuous plant preceded by a zero-order hold using an enhanced multiresolutional dynamic genetic algorithms (EMDGA) is proposed in this paper. Chromosomes consisting of the denominator and the numerator parameters of the reduced-order model are coded as a vector with floating point type components and searched by the genetic algorithm. Therefore, a stable optimal reduced-order model satisfying the error range specified can be evolutionarily obtained. Because of the use of the multiresolutional dynamic adaptation algorithm and genetic operators, the convergence rate of the evolution process to search for an optimal reduced-order model can be expedited. Another advantage of this approach is that the reduced discrete-time model evolves based on samples directly taken from the continuous plant, instead of the exact discrete-time model, so that computation time is saved.",
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