IDEAS home Printed from https://ideas.repec.org/a/gam/jmathe/v11y2023i12p2785-d1175556.html
   My bibliography  Save this article

Adaptive Super-Twisting Sliding Mode Control of Active Power Filter Using Interval Type-2-Fuzzy Neural Networks

Author

Listed:
  • Jiacheng Wang

    (College of Information Science and Engineering, Jiangsu Key Laboratory of Power Transmission and Distribution Equipment Technology, Hohai University, Changzhou 213022, China)

  • Yunmei Fang

    (College of Mechanical and Electrical Engineering, Hohai University, Changzhou 213022, China)

  • Juntao Fei

    (College of Information Science and Engineering, Jiangsu Key Laboratory of Power Transmission and Distribution Equipment Technology, Hohai University, Changzhou 213022, China)

Abstract

Aiming at the unknown uncertainty of an active power filter system in practical operation, combining the advantages of self-feedback structure, interval type-2 fuzzy neural network, and super-twisting sliding mode, an adaptive super-twisting sliding mode control method of interval type-2 fuzzy neural network with self-feedback recursive structure (IT2FNN-SFR STSMC) is proposed in this paper. IT2FNN has an uncertain membership function, which can enhance the nonlinear ability and robustness of the network. The historical information will be stored and utilized by the self-feedback recursive structure (SFR) at runtime. Therefore, the novel IT2FNN-SFR is designed to improve the dynamic approximation effect of the neural network and reduce the dependence of the controller on the actual mathematical model. The adaptive rate of each weight of the neural network is designed by the Lyapunov method and gradient descent (GD) algorithm to ensure the convergence and stability of the system. Super-twisting sliding mode control (STSMC) has strong robustness, which can effectively reduce system chattering, and improve control accuracy and system performance. The gain of the integral term in the STSMC is set as a constant, and the other gain is changed adaptively whose adaptive rate is deduced through the stability proof of the neural network, which greatly reduces the difficulty of parameter adjustment. The harmonic suppression ability of the designed control strategy is verified by simulation experiments.

Suggested Citation

  • Jiacheng Wang & Yunmei Fang & Juntao Fei, 2023. "Adaptive Super-Twisting Sliding Mode Control of Active Power Filter Using Interval Type-2-Fuzzy Neural Networks," Mathematics, MDPI, vol. 11(12), pages 1-20, June.
    • Handle: RePEc:gam:jmathe:v:11:y:2023:i:12:p:2785-:d:1175556
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-7390/11/12/2785/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2227-7390/11/12/2785/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Xiaofei Zhang & Hongbin Ma & Man Luo & Xiaomeng Liu, 2020. "Adaptive sliding mode control with information concentration estimator for a robot arm," International Journal of Systems Science, Taylor & Francis Journals, vol. 51(2), pages 217-228, January.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Lei Zhang & Xiangguo Li & Juntao Fei, 2023. "Self-Evolving Chebyshev Radial Basis Function Neural Complementary Sliding Mode Control," Mathematics, MDPI, vol. 11(14), pages 1-18, July.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Yin, Xiang & She, Jinhua & Wu, Min & Sato, Daiki & Ohnishi, Kouhei, 2022. "Disturbance rejection using SMC-based-equivalent-input-disturbance approach," Applied Mathematics and Computation, Elsevier, vol. 418(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jmathe:v:11:y:2023:i:12:p:2785-:d:1175556. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.