Self-Tuning Neuro-Fuzzy Generalized Minimum Variance Controller
| Authors: | Castillo Sergio Enrique Pinto, University of Strathclyde/Industrial Control Centre, Mexico
Grimble Mike J., University of Strathclyde/Industrial Control Centre, United Kingdom
Katebi Reza, University of Strathclyde/Industrial Control Centre, United Kingdom |
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| Topic: | 3.2 Cognition and Control ( AI, Fuzzy, Neuro, Evolut.Comp.) |
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| Session: | Adaptive Neuro-fuzzy Control |
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| Keywords: | Self-Tuning Control, Neuro-Fuzzy Modeling, Nonlinear Control |
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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.
