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Inverse fuzzy-process-model based direct adaptive control

Author

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  • Abonyi, János
  • Andersen, Hans
  • Nagy, Lajos
  • Szeifert, Ferenc

Abstract

This paper proposes a direct adaptive fuzzy-model-based control algorithm. The controller is based on an inverse semi-linguistic fuzzy process model, identified and adapted via input-matching technique. For the adaptation of the fuzzy model a general learning rule has been developed employing gradient-descent algorithm. The on-line learning ability of the fuzzy model allows the controller to be used in applications, where the knowledge to control the process does not exist or the process is subject to changes in its dynamic characteristics. To demonstrate the applicability of the method, a realistic simulation experiments were performed for a non-linear liquid level process. The proposed direct adaptive fuzzy logic controller is shown to be capable of handling non-linear and time-varying systems dynamics, providing good overall system performance.

Suggested Citation

  • Abonyi, János & Andersen, Hans & Nagy, Lajos & Szeifert, Ferenc, 1999. "Inverse fuzzy-process-model based direct adaptive control," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 51(1), pages 119-132.
    • Handle: RePEc:eee:matcom:v:51:y:1999:i:1:p:119-132
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    Cited by:

    1. Rana Muhammad Adnan & Zhongmin Liang & Xiaohui Yuan & Ozgur Kisi & Muhammad Akhlaq & Binquan Li, 2019. "Comparison of LSSVR, M5RT, NF-GP, and NF-SC Models for Predictions of Hourly Wind Speed and Wind Power Based on Cross-Validation," Energies, MDPI, vol. 12(2), pages 1-22, January.
    2. Hadi Sanikhani & Ozgur Kisi & Mohammad Nikpour & Yagob Dinpashoh, 2012. "Estimation of Daily Pan Evaporation Using Two Different Adaptive Neuro-Fuzzy Computing Techniques," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 26(15), pages 4347-4365, December.
    3. Jallal, Mohammed Ali & González-Vidal, Aurora & Skarmeta, Antonio F. & Chabaa, Samira & Zeroual, Abdelouhab, 2020. "A hybrid neuro-fuzzy inference system-based algorithm for time series forecasting applied to energy consumption prediction," Applied Energy, Elsevier, vol. 268(C).
    4. Hadi Sanikhani & Ozgur Kisi, 2012. "River Flow Estimation and Forecasting by Using Two Different Adaptive Neuro-Fuzzy Approaches," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 26(6), pages 1715-1729, April.

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