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Adaptive Neuro-Fuzzy Sliding Mode Controller

Author

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  • Sana Bouzaida

    (Industrial Systems Study and Renewable Energy (ESIER), National Engineering School of Monastir (ENIM), Monastir, Tunisia)

  • Anis Sakly

    (Industrial Systems Study and Renewable Energy (ESIER), National Engineering School of Monastir (ENIM), Monastir, Tunisia)

Abstract

A novel adaptive sliding mode controller using neuro-fuzzy network based on adaptive cooperative particle sub-swarm optimization (ACPSSO) is presented in this article for nonlinear systems control. The proposed scheme combines the advantages of adaptive control, neuro-fuzzy control, and sliding mode control (SMC) strategies without system model information. An adaptive training algorithm based on cooperative particle sub-swarm optimization is used for the online tuning of the controller parameters to deal with system uncertainties and disturbances. The algorithm was derived in the sense of Lyapunov stability analysis in order to guarantee the high quality of the controlled system. The performance of the proposed algorithm is evaluated against two well-known benchmark problems and simulation results that illustrate the effectiveness of the proposed controller.

Suggested Citation

  • Sana Bouzaida & Anis Sakly, 2018. "Adaptive Neuro-Fuzzy Sliding Mode Controller," International Journal of System Dynamics Applications (IJSDA), IGI Global, vol. 7(2), pages 34-54, April.
    • Handle: RePEc:igg:jsda00:v:7:y:2018:i:2:p:34-54
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    Cited by:

    1. Abhijit Bera & Mrinal Kanti Ghose & Dibyendu Kumar Pal, 2021. "Sentiment Analysis of Multilingual Tweets Based on Natural Language Processing (NLP)," International Journal of System Dynamics Applications (IJSDA), IGI Global, vol. 10(4), pages 1-12, October.

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