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Analyzing the performance of metaheuristic algorithms in speed control of brushless DC motor: Implementation and statistical comparison

  • 0Department of Electrical Engineering, University of Engineering and Technology, Lahore, Punjab, Pakistan.
Plos One +

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October 31, 2024

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October 31, 2024

View abstract on PubMed

Summary

This summary is machine-generated.

This study optimized proportional-integral (PI) controllers for brushless DC (BLDC) motor speed control using metaheuristic algorithms. The Whale Optimization Algorithm (WOA) demonstrated superior performance compared to other methods.

Area Of Science

  • Electrical Engineering
  • Control Systems
  • Computational Intelligence

Background

  • Brushless DC (BLDC) motors are crucial in industrial applications due to their efficiency and longevity.
  • Precise rotor orientation is vital for BLDC motor stability and performance.
  • Traditional proportional-integral (PI) controllers struggle with the nonlinear dynamics of BLDC motors.

Purpose Of The Study

  • To enhance BLDC motor speed control by optimizing PI controllers.
  • To evaluate the effectiveness of various metaheuristic algorithms for PI controller tuning.
  • To introduce a modified Levy Flight Trajectory-Based Whale Optimization Algorithm (LFWOA) with chaotic maps and weight factors (CMLFWOA).

Main Methods

  • Implementation of metaheuristic optimization techniques: Whale Optimization Algorithm (WOA), Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO), Accelerated Particle Swarm Optimization (APSO), and Levy Flight Trajectory-Based Whale Optimization Algorithm (LFWOA).
  • Development of a modified LFWOA (CMLFWOA) incorporating a chaotic map and weight factor.
  • Simulation of sensorless BLDC motor control models using MATLAB/Simulink.
  • Performance evaluation using Integral Square Error (ISE) criteria.
  • Comparative analysis and statistical testing using SPSS software.

Main Results

  • The Whale Optimization Algorithm (WOA) significantly outperformed other tested metaheuristic algorithms.
  • The proposed CMLFWOA also showed promising results in optimizing the PI controller.
  • Statistical analysis confirmed the superiority of WOA in achieving efficient BLDC motor speed control.

Conclusions

  • Metaheuristic optimization techniques offer a robust approach to improving PI controller performance for BLDC motors.
  • WOA is identified as a highly effective method for tuning PI controllers in BLDC motor applications.
  • The study provides a valuable comparison of optimization strategies for advanced motor control systems.

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