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

Authors

  • Nádson M. N. Lima,

    Corresponding author
    1. Faculty of Chemical Engineering, Dept. of Chemical Processes, State University of Campinas—UNICAMP, University City, Campinas, São Paulo 13081-970, Brazil
    • Faculty of Chemical Engineering, Dept. of Chemical Processes, State University of Campinas—UNICAMP, University City, Campinas, São Paulo 13081-970, Brazil
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  • Lamia Zuñiga Liñan,

    1. Faculty of Chemical Engineering, Dept. of Chemical Processes, State University of Campinas—UNICAMP, University City, Campinas, São Paulo 13081-970, Brazil
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  • Rubens Maciel Filho,

    1. Faculty of Chemical Engineering, Dept. of Chemical Processes, State University of Campinas—UNICAMP, University City, Campinas, São Paulo 13081-970, Brazil
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  • Maria R. Wolf Maciel,

    1. Faculty of Chemical Engineering, Dept. of Chemical Processes, State University of Campinas—UNICAMP, University City, Campinas, São Paulo 13081-970, Brazil
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  • Marcelo Embiruçu,

    1. Polytechnique Institute, Federal University of Bahia—UFBA, Rua Professor Aristides Novis, Federação, Salvador, Bahia 40210-630, Brazil
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  • Filipe Grácio

    1. Center of Biological and Chemical Engineering, Biotechnology and Bioengineering Institute—IBB, Lisbon 1049-001, Portugal
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Abstract

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

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