Self-tuning neuro-fuzzy generalized minimum variance controller

Sergio E. Pinto-Castillo, M.J. Grimble, M.R. Katebi

Research output: Contribution to conferencePaper

Abstract

The development of a Self-Tuning Neuro-Fuzzy Generalized Minimum Variance (GMV) controller is described. It uses fuzzy expert knowledge of the dynamic weightings to meet desired closed-loop stability and performance requirements. The controller is formulated in a polynomial system approach mixed with a Neuro-Fuzzy model and Fuzzy Self-Tuning mechanism. The proposed method is applied to a model of the Continuous Stirred Tank Reactor with Cooling Jacket and is compared with a PI controller, GMV controller with the correct model and a Fuzzy-PI controller. Simulation results are presented to demonstrate the performance of the proposed method.
Original languageEnglish
Publication statusPublished - 2005
Event16th IFAC World Congress Conference - Prague, Czech Republic
Duration: 4 Jul 20058 Jul 2005

Conference

Conference16th IFAC World Congress Conference
CountryCzech Republic
CityPrague
Period4/07/058/07/05

Fingerprint

Tuning
Controllers
Polynomials
Cooling

Keywords

  • self-tuning control
  • neuro-fuzzy modeling
  • nonlinear control

Cite this

Pinto-Castillo, S. E., Grimble, M. J., & Katebi, M. R. (2005). Self-tuning neuro-fuzzy generalized minimum variance controller. Paper presented at 16th IFAC World Congress Conference , Prague, Czech Republic.
Pinto-Castillo, Sergio E. ; Grimble, M.J. ; Katebi, M.R. / Self-tuning neuro-fuzzy generalized minimum variance controller. Paper presented at 16th IFAC World Congress Conference , Prague, Czech Republic.
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Pinto-Castillo, SE, Grimble, MJ & Katebi, MR 2005, 'Self-tuning neuro-fuzzy generalized minimum variance controller' Paper presented at 16th IFAC World Congress Conference , Prague, Czech Republic, 4/07/05 - 8/07/05, .

Self-tuning neuro-fuzzy generalized minimum variance controller. / Pinto-Castillo, Sergio E.; Grimble, M.J.; Katebi, M.R.

2005. Paper presented at 16th IFAC World Congress Conference , Prague, Czech Republic.

Research output: Contribution to conferencePaper

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AU - Grimble, M.J.

AU - Katebi, M.R.

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AB - The development of a Self-Tuning Neuro-Fuzzy Generalized Minimum Variance (GMV) controller is described. It uses fuzzy expert knowledge of the dynamic weightings to meet desired closed-loop stability and performance requirements. The controller is formulated in a polynomial system approach mixed with a Neuro-Fuzzy model and Fuzzy Self-Tuning mechanism. The proposed method is applied to a model of the Continuous Stirred Tank Reactor with Cooling Jacket and is compared with a PI controller, GMV controller with the correct model and a Fuzzy-PI controller. Simulation results are presented to demonstrate the performance of the proposed method.

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Pinto-Castillo SE, Grimble MJ, Katebi MR. Self-tuning neuro-fuzzy generalized minimum variance controller. 2005. Paper presented at 16th IFAC World Congress Conference , Prague, Czech Republic.