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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, Prague.

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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.

Item type: Conference or Workshop Item (Paper)
ID code: 11491
Notes: Requires Template change to Chapter in Book/Report/Conference proceeding › Conference contribution
Keywords: self-tuning control, neuro-fuzzy modeling, nonlinear control, Electrical engineering. Electronics Nuclear engineering
Subjects: Technology > Electrical engineering. Electronics Nuclear engineering
Department: Faculty of Engineering > Electronic and Electrical Engineering
Related URLs:
    Depositing user: Strathprints Administrator
    Date Deposited: 29 Jul 2011 09:55
    Last modified: 17 Jul 2013 15:19
    URI: http://strathprints.strath.ac.uk/id/eprint/11491

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