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Implicit GPC Based on Semi Fuzzy Neural Network Model

  • Conference paper
Intelligent Systems'2014

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 322))

Abstract

The model in Model Predictive Control (MPC) takes the central place. Therefore, it is very important to find a predictive model that effectively describes the behavior of the system and can easily be incorporated into MPC algorithm. In this paper it is presented implicit Generalized Predictive Controller (GPC) based on Semi Fuzzy Neural Network (SFNN) model. This kind of model works with reduced number of the fuzzy rules and respectively has low computational burden, which make it suitable for real-time applications like predictive controllers. Firstly, to demonstrate the potentials of the SFNN model test experiments with two benchmark chaotic systems - Mackey-Glass and Rossler chaotic time series are studied. After that, the SFNN model is incorporated in GPC and its efficiency is tested by simulation experiments in MATLAB environment to control a Continuous Stirred Tank Reactor (CSTR).

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Author information

Authors and Affiliations

  1. Institutes of Information and Communication Technologies, Bulgarian Academy of Sciences, Acad. G. Bonchev st., block 2, floor 5, 1113, Sofia, Bulgaria

    Margarita Terziyska & Lyubka Doukovska

  2. Technical University-Sofia, branch Plovdiv, 25, Ts. Dustabanov st., 4000, Plovdiv, Bulgaria

    Michail Petrov

Authors
  1. Margarita Terziyska
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  2. Lyubka Doukovska
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  3. Michail Petrov
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Corresponding author

Correspondence to Margarita Terziyska .

Editor information

Editors and Affiliations

  1. School of Computing and Communications, Lancaster University, Lancaster, United Kingdom

    P. Angelov

  2. Institute of Biophysics and Biomedical Engineering, Bulgarian Academy of Sciences, Sofia, Bulgaria

    K.T. Atanassov

  3. Intelligent Systems Department, Bulgarian Academy of Sciences Inst. of Infor. & Communication Techn., Sofia, Bulgaria

    L. Doukovska

  4. Metalurgy, University of Chemical Technology and, Sofia, Bulgaria

    M. Hadjiski

  5. University of Library Studies and IT (ULSIT), Sofia, Bulgaria

    V. Jotsov

  6. Systems Research Institute, Polish Academy of Sciences, Warsaw, Poland

    J. Kacprzyk

  7. Knowledge Engineering and Discovery Research Institute, Auckland University of Technology, Auckland, New Zealand

    N. Kasabov

  8. Intelligent Systems Laboratory, Prof. Assen Zlatarov University Faculty of Technical Sciences, Bourgas, Bulgaria

    S. Sotirov

  9. Systems Research Institute, Polish Academy of Sciences, Warsaw, Poland

    E. Szmidt

  10. Systems Research Institute, Polish Academy of Sciences, Warsaw, Poland

    S. Zadrożny

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© 2015 Springer International Publishing Switzerland

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Terziyska, M., Doukovska, L., Petrov, M. (2015). Implicit GPC Based on Semi Fuzzy Neural Network Model. In: Angelov, P., et al. Intelligent Systems'2014. Advances in Intelligent Systems and Computing, vol 322. Springer, Cham. https://doi.org/10.1007/978-3-319-11313-5_61

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  • DOI: https://doi.org/10.1007/978-3-319-11313-5_61

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-11312-8

  • Online ISBN: 978-3-319-11313-5

  • eBook Packages: EngineeringEngineering (R0)

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