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Abstract

In practical applications, permanent magnet synchronous motors’s benefits over brush-type motors, such as compact designs, high torque to inertia ratio, high power density, high air-gap flux density, and high effi-ciency. Moreover, they are also increasingly replacing induction motors in a variety of fields of application. Fast reaction, accurate control, and minimal overshoot feature represent crucial installation considerations while developing a control system for permanent magnet synchronous motors applications. Permanent magnet synchronous motor models are nonlinear and high-order concerning time-varying parameters, so a suitable controller is required. One well-known resilient model base control technique for systems with pa-rameter fluctuations and outside disturbances is sliding mode control. First, the sliding-mode controller is employed to reduce chattering and stabilize the PMSM drive system. On the other hand, modifications to the drive system's configuration and external disturbances might totally destroy the control performance. Therefore, this paper investigates combining neural networks with the sliding mode technique to propose an advanced robust permanent magnet synchronous motors velocity control algorithm. In order to increase the robustness and stability and decrease sliding mode chattering of the controller, our goal is to showcase sev-eral adaptive law designs for updating online sliding mode control laws and composite controller designs using radial basis function neural networks. The suggested adaptive neural network sliding mode controller based on the field-oriented scheme ANSMC-FOC technique, also known as the adaptive neural sliding mode based on a field-oriented control approach, confirms its complete accuracy and achieves outperform-ing control performance compared to PID and classical sliding mode control methods. Thus, the proposed permanent magnet synchronous motors velocity control algorithm is convincingly better than other perma-nent magnet synchronous motors' advanced speed control approaches.



Author's Affiliation
  • Nguyen Tien Dat

    Google Scholar Pubmed

  • Ho Pham Huy Anh

    Email I'd for correspondance: hphanh@hcmut.edu.vn
    Google Scholar Pubmed

Article Details

Issue: Vol 6 No 4 (2023)
Page No.: 2048-2059
Published: May 13, 2024
Section: Research article
DOI: https://doi.org/10.32508/stdjet.v6i4.1057

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Creative Commons License

Copyright: The Authors. This is an open access article distributed under the terms of the Creative Commons Attribution License CC-BY 4.0., which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

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 How to Cite
Dat, N. T., & Anh, H. P. H. (2024). Neural adaptive sliding mode with RBF applied in PMSM. VNUHCM Journal of Engineering and Technology, 6(4), 2048-2059. https://doi.org/https://doi.org/10.32508/stdjet.v6i4.1057

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