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

• Published in: AIChE journal Vol. 56; no. 4; pp. 965 - 978
• Main Authors: Lima, Nádson M.N, Liñan, Lamia Zuñiga, Filho, Rubens Maciel, Maciel, Maria R. Wolf, Embiruçu, Marcelo, Grácio, Filipe
• Format: Journal Article
• Language: English
• Published: Hoboken Wiley Subscription Services, Inc., A Wiley Company 01-04-2010; Wiley; American Institute of Chemical Engineers
• 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; model predictive control; fuzzy dynamic modeling; Control system; Prediction; Polymerization; System design; Copolymerization; Modeling; Fuzzy logic; model identification; Takagi-Sugeno model; Dynamic model; Mathematical model; Predictive control
• ISSN: 0001-1541; 1547-5905
• DOI: 10.1002/aic.12030

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