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Sensorless fuzzy control algorithm for permanent magnet synchronous motor based on particle swarm optimization parameter identification and harmonic extraction

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  • Kai Zhang
  • Lu Qing
  • Gai Liu
  • Li Quan

Abstract

In sensorless control of permanent magnet synchronous motor (IPMSM) with sliding mode observer, the problem of parameter robustness and poor stability seriously affects the control effect of sensorless operation. Therefore, a fuzzy sliding mode observer (SMO) phaselocked loop (PLL) combining particle swarm optimization (PSO) parameter identification and recursive least squares adaptive linear (RLS-Adaline) harmonic extractor is proposed. First, the fuzzy controller is used to process the control parameters of the sliding mode observer and the phaselocked loop. Secondly, the RLS-Adaline harmonic extractor is used to effectively filter the higher harmonic component of the back electromotive force (EMF), thus significantly improving the sensorless control effect. Thirdly, the PSO algorithm is used to identify the parameters of the motor, and the effect of parameter identification is judged by the performance index. The experimental results show that the proposed control method can effectively reduce the speed error and rotor position error.

Suggested Citation

  • Kai Zhang & Lu Qing & Gai Liu & Li Quan, 2024. "Sensorless fuzzy control algorithm for permanent magnet synchronous motor based on particle swarm optimization parameter identification and harmonic extraction," Journal of Electromagnetic Waves and Applications, Taylor & Francis Journals, vol. 38(8), pages 877-897, May.
    • Handle: RePEc:taf:tewaxx:v:38:y:2024:i:8:p:877-897
    DOI: 10.1080/09205071.2024.2343868
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