Discrete modeling of uncertain continuous systems having an interval structure using genetic algorithms

Chen Chien Hsu, Shih Chi Chang, Hsin Yen Kuo

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

In this paper, an evolutionary approach is proposed to obtain the discrete-time transfer function for uncertain continuous-time systems having interval uncertainties. Based on a worst-case analysis, the problem to derive the discrete-time model is first formulated as multiple mono-objective optimization problems for coefficients in the discrete model, and subsequently minimized and maximized via a proposed genetic algorithm to obtain the lower and upper bounds of the coefficient functions. The problem of non-linearly coupled coefficients with exponential nature occurred in the exact discrete-time transfer function is therefore circumvented while preserving the interval structure in the resulting discrete model by using this approach. Because of the time-consuming process that genetic algorithms generally exhibit, particularly the problem nature which requires undertaking a large number of evolution processes, parallel computation for the proposed evolutionary approach in a MATLAB-based working environment is therefore proposed to accelerate the derivation process.

Original languageEnglish
Title of host publicationProceedings of the IASTED International Conference on Computational Intelligence
Pages310-315
Number of pages6
Publication statusPublished - 2005 Dec 1
EventIASTED International Conference on Computational Intelligence - Calgary, AB, Canada
Duration: 2005 Jul 42005 Jul 6

Publication series

NameProceedings of the IASTED International Conference on Computational Intelligence
Volume2005

Other

OtherIASTED International Conference on Computational Intelligence
Country/TerritoryCanada
CityCalgary, AB
Period2005/07/042005/07/06

Keywords

  • Discrete modeling
  • Discretization
  • Genetic algorithms
  • Interval plant
  • Parallel computation
  • Sampled-data systems
  • Uncertain systems

ASJC Scopus subject areas

  • Engineering(all)

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