Self-tuning neuro-fuzzy generalized minimum variance controller

Pinto-Castillo, Sergio E. and Grimble, M.J. and Katebi, M.R. (2005) Self-tuning neuro-fuzzy generalized minimum variance controller. In: 16th IFAC World Congress Conference, 2005-07-04 - 2005-07-08. (http://www.ifac-papersonline.net/Detailed/28376.ht...)

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.

ORCID iDs

Katebi, M.R. ORCID: https://orcid.org/0000-0003-2729-0688

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• Item metadata

  Item type:	Conference or Workshop Item(Paper)
  ID code:	11491
  Dates:	Date Event 2005 Published
  Notes:	Requires Template change to Chapter in Book/Report/Conference proceeding › Conference contribution
  Subjects:	Technology > Electrical engineering. Electronics Nuclear engineering
  Department:	Faculty of Engineering > Electronic and Electrical Engineering
  Depositing user:	Strathprints Administrator
  Date deposited:	29 Jul 2011 08:55
  Last modified:	11 Nov 2024 16:18
  URI:	https://strathprints.strath.ac.uk/id/eprint/11491