摘要
针对传统无刷直流电机(brushless direct current motor, BLDCM)无位置传感器控制在低速起动阶段由于反电动势幅值较小导致换相信号精度下降,进而引发换相失败的问题,本文提出了一种基于河马算法优化BP神经网络的无感控制方法。该方法以电机三相电压与电流信号为输入,构建非线性映射模型,实现对电机精确换相信号的估算。仿真结果表明,本文提出的基于河马优化算法的BP神经网络无位置传感器控制策略在无刷直流电机系统中表现出良好的性能。能够实现准确的换向信号估计,具备快速、平稳的调速特性,系统动态响应良好。
To address the problem in traditional sensorless control of brushless direct current motor(BLDCM),where the accuracy of commutation signals significantly decreases during low-speed startup due to the weak back electromotive force,which can lead to commutation failure,this paper proposes a sensorless control method based on a backpropagation neural network optimized by the hippopotamus optimization(HO)algorithm.The proposed method takes the three-phase voltage and current signals of the motor as inputs to construct a nonlinear mapping model,enabling accurate estimation of the commutation signals.Simulation results show that the proposed HO-BP neural network sensorless control method performs well in the BLDCM system.It can accurately estimate commutation signals,achieve fast and smooth speed control,and has good dynamic response.
作者
于林鑫
杨锴堃
YU Linxin;YANG Kaikun(School of Automation and Electrical Engineering,Shenyang Ligong University,Shenyang 110159,China)
出处
《微特电机》
2025年第12期1-6,共6页
Small & Special Electrical Machines
基金
辽宁省教育厅高等学校基本科研项目(JYTQN2023063)。
关键词
无刷直流电机
河马优化算法
BP神经网络
无感控制
brushless direct current motor
hippopotamus optimization algorithm
BP neural network
sensorless control
