基于神经元控制的无刷电机调速建模仿真

Modeling and Simulation of Brushless Motor Speed Regulation Based on Neuron Control

现有无刷电机调速方法容易陷入局部极小值,且收敛速度较慢,存在着无刷电机调速实时性差、转矩波动幅度小等缺陷,故提出基于神经元控制算法的无刷电机调速建模仿真研究。为了更加精确的对无刷电机调速进行控制,采用神经网络模型辨识并估计无刷电机参数,构建无刷电机数学参数优化模型,以经过28335DSP芯片处理后的无刷电机数字信号为基础,执行神经元控制算法,即可实现无刷电机调速控制。仿真结果显示,所提方法调速实时性更好,转矩波动幅度更大,其无刷电机调速效果更好,适合大力推广。

The current brushless motor speed regulation method has slow convergence speed, poor real-time performance of motor speed regulation, small torque fluctuation, and easy to fall into a local minimum. This paper reports the modeling and Simulation of brushless motor speed regulation based on a neuron control algorithm for improving the performance of the brushless motor speed regulation method. For more accurately controlling the speed regulation of brushless motor and establishing the mathematical parameter optimization model of brushless motor, a neural network model was applied to identify and estimate the parameters of brushless motor. Based on the digital signal of the brushless motor processed by the 28335 DSP chip, the neuron control algorithm was implemented to complete the speed regulation control of the brushless motor. The simulation results show that this method has excellent real-time speed regulation, large torque fluctuation, and excellent speed regulation effect.