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<p>Brushless DC (BLDC) motors are popular in e-mobility, home appliances, and industrial sectors due to their reliability, high efficiency, and adaptable speed ranges. Effective speed controller design is crucial for these motors operating under various conditions, such as constant loads, variable loads, and fluctuating set speeds. Traditional controllers, including proportional-integral (PI), often fail to meet efficiency expectations due to the inherent nonlinearity of BLDC motor drives. This paper introduces the heuristic adaptive lyrebird optimization algorithm (HA-LOA), a novel optimization approach inspired by the mimetic and adaptive behaviors of lyrebirds, which integrates three movement strategies to enhance convergence and solution accuracy for PI controller tuning. The proposed HA-LOA’s efficiency is validated through benchmark tests and experimental work including comparison with the traditional control methods such as PID, ANFIS, hybrid grey wolf optimization-PI, and fast-firefly-PI. The HA-LOA excelled in all four modes, particularly in Mode 4, due to its exceptional accuracy, stability, and overall speed performance metrics, especially with an overshoot of ~ 0% and a very short rise time of 0.110&#xa0;s and significant reduction in ITAE (99.84%), IAE (99.53%), IE (99.50%), ISE (53.03%) and ITSE (99.94%). The experimental results demonstrate that the proposed one achieved identical values with the simulation attaining a speed of 1500.1 RPM, validating its superior capability in applications requiring precise speed control.</p>

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Optimized PI controller tuning for improved performance in BLDC motor speed control using heuristic adaptive lyrebird optimization algorithm

  • 期刊论文

  • 发表日期：

    2025年2月6日

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  • Amal Mohamad Jarkas,
  • M. Arun Noyal Doss

摘要

Brushless DC (BLDC) motors are popular in e-mobility, home appliances, and industrial sectors due to their reliability, high efficiency, and adaptable speed ranges. Effective speed controller design is crucial for these motors operating under various conditions, such as constant loads, variable loads, and fluctuating set speeds. Traditional controllers, including proportional-integral (PI), often fail to meet efficiency expectations due to the inherent nonlinearity of BLDC motor drives. This paper introduces the heuristic adaptive lyrebird optimization algorithm (HA-LOA), a novel optimization approach inspired by the mimetic and adaptive behaviors of lyrebirds, which integrates three movement strategies to enhance convergence and solution accuracy for PI controller tuning. The proposed HA-LOA’s efficiency is validated through benchmark tests and experimental work including comparison with the traditional control methods such as PID, ANFIS, hybrid grey wolf optimization-PI, and fast-firefly-PI. The HA-LOA excelled in all four modes, particularly in Mode 4, due to its exceptional accuracy, stability, and overall speed performance metrics, especially with an overshoot of ~ 0% and a very short rise time of 0.110 s and significant reduction in ITAE (99.84%), IAE (99.53%), IE (99.50%), ISE (53.03%) and ITSE (99.94%). The experimental results demonstrate that the proposed one achieved identical values with the simulation attaining a speed of 1500.1 RPM, validating its superior capability in applications requiring precise speed control.