无刷直流电机免疫反馈自适应学习人工神经网络控制

针对BP神经网络收敛速度较慢、学习时间较长的缺陷，提出了基于免疫反馈规则的神经网络控制器用于无刷直流电机的转速控制中．该规则根据免疫系统中的T细胞的生物免疫反馈机理而总结出来，包括决定应答速度的激活环节和决定稳定效果的抑制环节．将免疫反馈规则应用于BP神经网络训练中学习速率的自适应调节，加快了神经网络的收敛速度，缩短了学习时间．无刷直流电机控制系统采用双闭环控制，内环为电流环，外环为速度环．MATLAB仿真和实验表明，采用该方法的系统超调量小、速度响应快，而且速度响应受电机参数变化影响小，各种外界干扰也得到了很好的抑制，具有较高的控制精度和较好的动、静态性能．

Artificial Neural Network Control of Brushless DC Motor Based on Immune Feedback Adaptive Learning

An artificial neural network(ANN) controller based on the immune feedback law,was put forward for the speed control of brushless DC motor(BLDCM).The immune feedback law obtained from the biological immune feedback mechanism of T-cells,includes an active term,Which controls response speed,and an inhibitive term,which controls stabilization effect.when the immune feedback law was applied to the regulation of learning rate,it speeds up the convergence rate of the neural network learning algorithms,thus improving the learning performance.The BLDCM control system includes current closed loop and speed closed loop.The simulation and experiments illustratethat excellent static and dynamic performance,adaptability as well as high precision can be achieved by the proposed strategy.