A design method of extended generalized minimum variance control based on state space approach by using a genetic algorithm

Akira Yanou, Mingcong Deng, Akira Inoue

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

Abstract

This paper explores a selection method of the design parameter introduced in the extended Generalized Minimum Variance Control (GMVC) based on state-space approach by using a genetic algorithm. The extended controller has a new design parameter which can design the controller poles without changing the closed-loop poles, and the genetic algorithm is applied to find the new design parameter and select the controller poles.

Original languageEnglish
Title of host publication2009 4th International Conference on Innovative Computing, Information and Control, ICICIC 2009
Pages189-192
Number of pages4
DOIs
Publication statusPublished - Dec 1 2009
Event2009 4th International Conference on Innovative Computing, Information and Control, ICICIC 2009 - Kaohsiung, Taiwan, Province of China
Duration: Dec 7 2009Dec 9 2009

Publication series

Name2009 4th International Conference on Innovative Computing, Information and Control, ICICIC 2009

Other

Other2009 4th International Conference on Innovative Computing, Information and Control, ICICIC 2009
CountryTaiwan, Province of China
CityKaohsiung
Period12/7/0912/9/09

Keywords

  • Coprime factorization
  • Generalized minimum variance control
  • Genetic algorithm
  • State space

ASJC Scopus subject areas

  • Computational Theory and Mathematics
  • Computer Networks and Communications
  • Software

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    Yanou, A., Deng, M., & Inoue, A. (2009). A design method of extended generalized minimum variance control based on state space approach by using a genetic algorithm. In 2009 4th International Conference on Innovative Computing, Information and Control, ICICIC 2009 (pp. 189-192). [5412488] (2009 4th International Conference on Innovative Computing, Information and Control, ICICIC 2009). https://doi.org/10.1109/ICICIC.2009.7