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An Adaptive Fuzzy Predictive Control Based on Support Vector Regression

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Advanced Control Engineering Methods in Electrical Engineering Systems (ICEECA 2017)

Part of the book series: Lecture Notes in Electrical Engineering ((LNEE,volume 522))

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Abstract

In this paper, an adaptive fuzzy Generalized Predictive Control (GPC) is proposed for nonlinear systems via Takagi-Sugeno system based Support Vector Regression (TS-SVR). The adaptive T-S fuzzy model is created using a support vector regression while the online learning procedure is obtained in two steps: first, the antecedent parameters of the TS-SVR are initialized using a k-means clustering and then iteratively adjusted using a back-propagation algorithm. Next, a sequential minimal optimization (SMO) algorithm is used to obtain the consequent parameters. Furthermore, the new TS fuzzy model is integrated into the GPC in order to control nonlinear systems. The performance of the proposed adaptive TS-SVR GPC controller is investigated by controlling the continuous stirred tank reactor (CSTR) system. The proposed TS-SVR GPChas shown good performance and efficiently controlled the nonlinear plant.

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Author information

Authors and Affiliations

  1. Institute for Intelligent Systems, University of Johannesburg, Johannesburg, South Africa

    I. Boulkaibet, T. Marwala, B. Twala & A. Ali

  2. Faculty of Sciences and Technology, University Abbes Laghrour, Khenchela, Algeria

    S. Bououden

Authors
  1. I. Boulkaibet
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  2. S. Bououden
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  3. T. Marwala
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  4. B. Twala
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  5. A. Ali
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Corresponding author

Correspondence to I. Boulkaibet .

Editor information

Editors and Affiliations

  1. MIS (EA 4290), Université de Picardie Jules Verne, Amiens, France

    Mohammed Chadli

  2. Faculty of Sciences and Technology, University Abbes Laghrour Khenchela, Khenchela, Algeria

    Sofiane Bououden

  3. Faculty of Sciences and Technology, University of Constantine, Constantine, Algeria

    Salim Ziani

  4. Department of Computer Science, Faculty of Electrical Engineering and Computer Science, VÅ B-TUO, Ostrava-Poruba, Czech Republic

    Ivan Zelinka

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Boulkaibet, I., Bououden, S., Marwala, T., Twala, B., Ali, A. (2019). An Adaptive Fuzzy Predictive Control Based on Support Vector Regression. In: Chadli, M., Bououden, S., Ziani, S., Zelinka, I. (eds) Advanced Control Engineering Methods in Electrical Engineering Systems. ICEECA 2017. Lecture Notes in Electrical Engineering, vol 522. Springer, Cham. https://doi.org/10.1007/978-3-319-97816-1_14

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