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Adaptive Fuzzy Neural Network Harmonic Control with a Super-Twisting Sliding Mode Approach

Author

Listed:
  • Qi Pan

    (College of IoT Engineering, Hohai University, Changzhou 213022, China)

  • Xiangguo Li

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

  • Juntao Fei

    (College of IoT Engineering, Hohai University, Changzhou 213022, China
    College of Mechanical and Electrical Engineering, Hohai University, Changzhou 213022, China
    Jiangsu Key Lab. of Power Transmission and Distribution Equipment Technology, Hohai University, Changzhou 213022, China)

Abstract

This paper designed an adaptive super-twisting sliding mode control (STSMC) scheme based on an output feedback fuzzy neural network (OFFNN) for an active power filter (APF), aiming at tracking compensation current quickly and precisely, and solving the harmonic current problem in the electrical grid. With the use of OFFNN approximator, the proposed controller has the characteristic of full regulation and high approximation accuracy, where the parameters of OFFNN can be adjusted to the optimal values adaptively, thereby increasing the versatility of the control method. Moreover, due to an added signal feedback loop, the controller can obtain more information to track the state variable faster and more correctly. Simulations studies are given to demonstrate the performance of the proposed controller in the harmonic suppression, and verify its better steady-state and dynamic performance.

Suggested Citation

  • Qi Pan & Xiangguo Li & Juntao Fei, 2022. "Adaptive Fuzzy Neural Network Harmonic Control with a Super-Twisting Sliding Mode Approach," Mathematics, MDPI, vol. 10(7), pages 1-18, March.
    • Handle: RePEc:gam:jmathe:v:10:y:2022:i:7:p:1063-:d:779723
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    Cited by:

    1. Qi Pan & Yanli Zhou & Juntao Fei, 2023. "Feature Selection Fuzzy Neural Network Super-Twisting Harmonic Control," Mathematics, MDPI, vol. 11(6), pages 1-19, March.

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