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Robust neuro-fuzzy sliding mode control with extended state observer for an electric drive system

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  • Bouguenna, Ibrahim Farouk
  • Azaiz, Ahmed
  • Tahour, Ahmed
  • Larbaoui, Ahmed

Abstract

The choice of the control techniques has a positive impact on the traction chain particularly disturbance rejection ability and the energy management in the electric vehicle (electric motor, inverter, transmission, ect.). In this paper, a robust neuro-fuzzy-sliding mode control (RNFSMC) with extended state observer (ESO) technique is applied on the traction chain of the electric vehicle (Permanent magnet synchronous motor PMSM, Inverter, Transmission). However, most of the existing strategies of control that are applied on the traction chain lead to chattering phenomena, reducing the electric motor performance and disturbance rejection ability without forgetting the bad energy management on board the electric vehicle. To further enhance the performance of the traction chain, a hybrid control scheme is used to severally decrease the chattering phenomena in the PMSM electric motor and evolve the disturbance rejection ability which employs two types of controllers: Neuro-fuzzy sliding mode control on the direct current loop and ESO controller on both speed, and quadrature current loops taking into account the dynamic of the vehicle. Simulations by Matlab/Simulink are used to indicate the validity of the planed scheme on the closed-loop system. The simulation results show the effectiveness of the proposed control strategy with desired tracking accuracy.

Suggested Citation

  • Bouguenna, Ibrahim Farouk & Azaiz, Ahmed & Tahour, Ahmed & Larbaoui, Ahmed, 2019. "Robust neuro-fuzzy sliding mode control with extended state observer for an electric drive system," Energy, Elsevier, vol. 169(C), pages 1054-1063.
    • Handle: RePEc:eee:energy:v:169:y:2019:i:c:p:1054-1063
    DOI: 10.1016/j.energy.2018.12.101
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    Cited by:

    1. Ibrahim Farouk Bouguenna & Ahmed Tahour & Ralph Kennel & Mohamed Abdelrahem, 2021. "Multiple-Vector Model Predictive Control with Fuzzy Logic for PMSM Electric Drive Systems," Energies, MDPI, vol. 14(6), pages 1-23, March.
    2. Hu, Jianjun & Guo, Qi & Sun, Zhicheng & Yang, Dianzhao, 2023. "Study on low-frequency torsional vibration suppression of integrated electric drive system considering nonlinear factors," Energy, Elsevier, vol. 284(C).

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