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RBF Neural Network-Based Sliding Mode Control for Modular Multilevel Converter with Uncertainty Mathematical Model

Author

Listed:
  • Xuhong Yang

    (College of Automation Engineering, Shanghai University of Electric Power, Shanghai 200090, China)

  • Haoxu Fang

    (College of Automation Engineering, Shanghai University of Electric Power, Shanghai 200090, China)

Abstract

For medium and high-powered applications, modular multilevel converters have become the most promising converter application. In this paper, a sliding mode controller based on an RBF neural network is proposed for a modular multilevel converter. The RBF neural network is designed to approximate the uncertainty mathematical model of a modular multilevel converter. The main innovation of the proposed method is that it does not require any model parameters and control parameters during the whole control process. This means that parameter changes caused by the external environment will not influence the controller performances. Finally, by comparing with a conventional PI controller, the simulation proves the feasibility and effectiveness of the proposed control method. In addition, the experimental results show that the grid-side current can become stable immediately while the active power is stabilized after 20 ms when the set value is changed.

Suggested Citation

  • Xuhong Yang & Haoxu Fang, 2022. "RBF Neural Network-Based Sliding Mode Control for Modular Multilevel Converter with Uncertainty Mathematical Model," Energies, MDPI, vol. 15(5), pages 1-18, February.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:5:p:1634-:d:755894
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    References listed on IDEAS

    as
    1. Weide Guan & Shoudao Huang & Derong Luo & Fei Rong, 2019. "A Reverse Model Predictive Control Strategy for a Modular Multilevel Converter," Energies, MDPI, vol. 12(2), pages 1-15, January.
    2. Zhi Wu & Jiawei Chu & Wei Gu & Qiang Huang & Liang Chen & Xiaodong Yuan, 2018. "Hybrid Modulated Model Predictive Control in a Modular Multilevel Converter for Multi-Terminal Direct Current Systems," Energies, MDPI, vol. 11(7), pages 1-17, July.
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