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Updated: Sep 13, 2025

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Enhancing power efficiency in BLDC motor drives for drones using multiview learning with hybrid optimization

Anushree Gopalakrishnan1, Rani Thottungal2

  • 1Department of Electrical and Electronics Engineering, Kumaraguru College of Technology, Coimbatore, 641049, India. anushree.g.eee@gmail.com.

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|August 1, 2025
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Summary

This study enhances Brushless Direct Current (BLDC) motor drives for drones using bio-inspired optimization. Eel and barnacle foraging methods significantly improve power efficiency and performance metrics compared to standard algorithms.

Keywords:
BLDC motor drivesDrone applicationsElectric eel foragingGooseneck barnacle optimizationMulti-view learningPower efficiency

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Area of Science:

  • Electrical Engineering
  • Robotics
  • Optimization Algorithms

Background:

  • Brushless Direct Current (BLDC) motor drives are crucial for drone power systems.
  • Existing optimization methods for BLDC drives have limitations in power efficiency.
  • Bio-inspired algorithms offer novel approaches to complex engineering problems.

Purpose of the Study:

  • To enhance power efficiency in BLDC motor drives for drone applications.
  • To evaluate the effectiveness of Eel Foraging and Gooseneck Barnacle Optimization (GBO) techniques.
  • To establish optimal performance benchmarks for future BLDC motor-drive designs.

Main Methods:

  • Utilized multi-view learning techniques to evaluate performance metrics.
  • Applied bio-inspired optimization algorithms: Eel Foraging and Gooseneck Barnacle Optimization (GBO).
  • Employed mathematical optimization for adaptive adjustments based on fitness values and previous states.

Main Results:

  • Eel Foraging (0.05) and GBO (0.04) showed superior fitness improvement rates per iteration compared to standard algorithms (0.02-0.10).
  • Demonstrated significant improvements in torque-speed characteristics and power ratings.
  • Eel Foraging generated 15 Nm torque at 2000 rpm, GBO generated 14 Nm.

Conclusions:

  • Eel Foraging and GBO, enhanced by multi-view learning, offer substantial power efficiency gains for BLDC motor drives.
  • These bio-inspired methods provide adaptable and continuously improving solutions.
  • The study sets new performance benchmarks for efficient drone motor drive design.