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.
Full text not available in this repository. (Request a copy from the Strathclyde author)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: | 04 Oct 2012 17:16 |
| URI: | http://strathprints.strath.ac.uk/id/eprint/11491 |
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