摘要
直流无刷电机(BLDCM)具有结构简单、运行可靠、节能、线性更佳等优点,但因其自身控制复杂及存在转矩脉动等特性,传统PID控制难以满足高精度转速控制需求。将神经网络算法与PID控制相结合,利用BP算法训练神经网络,进而通过神经网络的自学习与自适应能力优化PID控制器参数。结果表明,该方法显著提升了BLDCM转速控制的响应速度、稳定性及抗干扰能力,有效克服了传统PID控制的局限性。
Brushless DC Motors(BLDCMs)offer advantages such as simple structure,reliable operation,energy efficiency,and better linearity.However,due to their inherent control complexity and characteristics like torque ripple,traditional PID control struggles to meet the demands for high-precision speed control.By combining neural network algorithms with PID control,the neural network is trained using the BP algorithm.Subsequently,the self-learning and adaptive capabilities of the neural network are utilized to optimize the parameters of the PID controller.It is demonstrated that this method significantly enhances the response speed,stability,and anti-interference ability of speed control for BLDCMs,effectively overcoming the limitations of traditional PID control.
作者
马琨伦
王建军
MA Kunlun;WANG Jianjun(College of Electronic&Electrical Engineering,Shanghai Second Polytechnic University,Shanghai 201209,China)
出处
《自动化应用》
2025年第22期153-155,160,共4页
Automation Application
