Modeling and predictive control using fuzzy logic: Application for a polymerization system

In this study, a predictive control system based on type Takagi-Sugeno fuzzy models was developed for a polymerization process. Such processes typically have a highly nonlinear dynamic behavior causing the performance of controllers based on conventional internal models to be poor or to require cons...

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Bibliographic Details
Published in:AIChE journal 2010-04, Vol.56 (4), p.965-978
Main Authors: Lima, Nádson M.N, Liñan, Lamia Zuñiga, Filho, Rubens Maciel, Maciel, Maria R. Wolf, Embiruçu, Marcelo, Grácio, Filipe
Format: Article
Language:English
Subjects:
Applied sciences
Chemical engineering
Control systems
Controllers
Copolymerization
Dynamical systems
Exact sciences and technology
fuzzy dynamic modeling
Fuzzy logic
Mathematical models
model identification
model predictive control
Nonlinear dynamics
Organic polymers
Physicochemistry of polymers
Polymerization
Predictive control
Preparation, kinetics, thermodynamics, mechanism and catalysts
Studies
Takagi-Sugeno model
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Summary:In this study, a predictive control system based on type Takagi-Sugeno fuzzy models was developed for a polymerization process. Such processes typically have a highly nonlinear dynamic behavior causing the performance of controllers based on conventional internal models to be poor or to require considerable effort in controller tuning. The copolymerization of methyl methacrylate with vinyl acetate was considered for analysis of the performance of the proposed control system. A nonlinear mathematical model which describes the reaction plant was used for data generation and implementation of the controller. The modeling using the fuzzy approach showed an excellent capacity for output prediction as a function of dynamic data input. The performance of the projected control system and dynamic matrix control for regulatory and servo problems were compared and the obtained results showed that the control system design is robust, of simple implementation and provides a better response than conventional predictive control. © 2009 American Institute of Chemical Engineers AIChE J, 2010
ISSN:0001-1541
1547-5905
DOI:10.1002/aic.12030