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A unified control method combined with improved TSF and LADRC for SRMs Using modified grey wolf optimization

Xiaodong Sun1, Yefei Xiong1, Ming Yao2

  • 1Automotive Engineering Research Institute, Jiangsu University, Zhenjiang 212013, China.

ISA Transactions
|June 7, 2022
PubMed
Summary
This summary is machine-generated.

This study introduces a novel control method for multimode switched reluctance motors (SRMs) using an enhanced grey wolf optimization algorithm. This approach improves performance across various operating conditions by reducing torque ripple and enhancing anti-interference capabilities.

Keywords:
An improved grey wolf optimization algorithm (IGWOA)Linear active disturbance rejection control (LADRC)Switched reluctance motor (SRM)Torque sharing function

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Area of Science:

  • Electrical Engineering
  • Control Systems
  • Optimization Algorithms

Background:

  • Traditional switched reluctance motors (SRMs) have limitations in performance under varying operating conditions.
  • Multimode SRMs offer flexibility, operating as either three-phase or six-phase motors.
  • Existing control methods may struggle with smooth mode switching and optimal performance.

Purpose of the Study:

  • To propose a unified control method for 12/10 pole multimode switched reluctance motors (SRMs).
  • To enhance the performance and adaptability of SRMs across diverse operational scenarios.
  • To ensure smooth transitions between different operating modes.

Main Methods:

  • An improved grey wolf optimization algorithm, incorporating a coyote group, was developed for global optimization.
  • In Torque Sharing function (TSRM) mode, a parameter-tuned torque sharing function minimizes torque ripple and expands the speed range.
  • In Single-phase Switched Reluctance Motor (SSRM) mode, Linear Active Disturbance Rejection Control (LADRC) enhances system robustness.

Main Results:

  • The proposed method effectively reduces torque ripple in TSRM mode.
  • The LADRC method significantly improves the anti-interference capabilities in SSRM mode.
  • The improved grey wolf optimization algorithm ensures seamless switching between motor modes.
  • Experimental validation confirmed the efficacy of the unified control strategy.

Conclusions:

  • The unified control method provides superior performance for multimode SRMs.
  • The integration of improved optimization algorithms and advanced control techniques addresses key operational challenges.
  • This research offers a robust solution for controlling multimode SRMs in demanding applications.