Purpose

The brushless direct current motor (BLDCM) is widely accepted and adopted by many industries instead of direct current motors due to high reliability during operation. Brushless direct current (BLDC) has outstanding efficiency as losses that arise out of voltage drops at brushes and friction losses are eliminated. The main factor that affects the performance is temperature introduced in the internal copper core windings. The control of motor speed generates high temperature in BLDC operation. The high temperature is due to presence of ripples in the operational current. The purpose is to present an effective controlling mechanism for speed management and to improve the performance of BLDCM to activate effective management of speed.

Design/methodology/approach

The purpose is to present an optimal algorithm based on modified moth-flame optimization algorithm over recurrent neural network (MMFO-RNN) for speed management to improve the performance. The core objective of the presented work is to achieve improvement in performance without affecting the design of the system with no additional circuitry. The management of speed in BLDCM has been achieved through reduction or minimization of ripples encircled with torque of the motor. The implementation ends in two stages, namely, controlling the loop of torque and controlling the loop of speed. The MMFO-RNN starts with error optimization, which arises from both the loops, and most effective values have been achieved through MMFO-RNN protocol.

Findings

The parameters are enriched with Multi Resolution Proportional Integral and Derivative (MRPID) controller operation to achieve minimal ripples for the torque of BLDC and manage the speed of the motor. The performance is increased by adopting this technique approximately 12% in comparison with the existing methodology, which is the main contributions of the presented work. The outcomes are analyzed with the existing methodologies through MATLAB Simulink tool, and the comparative analyses suggest that better performance of the proposed system produces over existing techniques, and proto type model is developed and cross verifies the proposed system.

Originality/value

The MMFO-RNN starts with error optimization, which arises from both the loops, and most effective values have been achieved through MMFO-RNN protocol. The parameters are enriched with MRPID controller operation to achieve nil or minimal ripples and to encircle the torque of Brushless Direct Current and manage the speed.

中文翻译：

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基于ARM单片机的MV-PID控制器管理系统的无刷直流电机

目的

无刷直流电机（BLDCM）因其在运行过程中的高可靠性而被许多行业广泛接受和采用，而不是直流电机。无刷直流电 (BLDC) 具有出色的效率，因为消除了由电刷处的电压降和摩擦损耗引起的损耗。影响性能的主要因素是内部铜芯绕组中引入的温度。电机速度的控制会在 BLDC 运行中产生高温。高温是由于工作电流中存在纹波。目的是为速度管理提供有效的控制机制，并提高BLDCM的性能以激活有效的速度管理。

设计/方法/方法

目的是提出一种基于改进的蛾火焰优化算法在递归神经网络（MMFO-RNN）上的优化算法，用于速度管理以提高性能。所提出工作的核心目标是在不影响系统设计的情况下实现性能改进，而无需额外的电路。BLDCM 中的速度管理是通过减少或最小化电机转矩所环绕的纹波来实现的。实现分两个阶段结束，即控制转矩环和控制速度环。MMFO-RNN 从两个循环中产生的误差优化开始，并且最有效的值是通过 MMFO-RNN 协议实现的。

发现

这些参数通过多分辨率比例积分和微分 (MRPID) 控制器操作进行了丰富，以实现 BLDC 转矩的最小纹波并管理电机的速度。与现有方法相比，采用这种技术提高了大约 12% 的性能，这是所提出工作的主要贡献。通过 MATLAB Simulink 工具与现有方法对结果进行了分析，比较分析表明，所提出的系统比现有技术具有更好的性能，并开发了原型模型并交叉验证了所提出的系统。

原创性/价值

MMFO-RNN 从两个循环中产生的误差优化开始，并且最有效的值是通过 MMFO-RNN 协议实现的。参数通过 MRPID 控制器操作丰富，以实现零或最小纹波，并环绕无刷直流电流的扭矩和管理速度。