Self-tuning neuro-fuzzy generalized minimum variance controller

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

Research output: Contribution to conferencePaper


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


Conference16th IFAC World Congress Conference
Country/TerritoryCzech Republic


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


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