基于神经网络的五相内置式永磁电动机的模型参考自适应控制

摘要

提出了一种新的对五相内置式永磁电动机拖动的模型参考自适应控制方法.主控制器是基于人工神经网络设计的,这种人工神经网路可以在不知道电动机模型的准确参数时来模拟系统的非线性特征值.因为所提出的电动机拖动方式是应用多重参考坐标转换来实现对转矩的解耦和五相内置式永磁电动机的多路输出,所以,电动机能够通过最大转矩电流比控制实现低于额定转速运行,也可以通过弱磁控制实现高于额定转速运行.主控制器的神经网络采用径向基函数网路,径向基函数网络可以在线训练来适应系统的动态性.完整的永磁同步电动机驱动是用Matlab/Simulink作仿真.%This paper presents a novel model reference adaptive control (MRAC) of a five-phase interior permanent magnet (IPM) motor drives. The primary controller is designed based on artificial neural network (ANN) to simulate the nonlinear characteristics of the system without the knowledge of the accurate parameters of motor model. The proposed motor drive decouples the torque and flux components of five-phase IPM motors by applying multiple reference frame transformation. Therefore, the motor can be easily driven below the rated speed with the maximum torque per ampere (MTPA) operation or above the rated speed with the flux weakening operation. The ANN based primary controller consists of a radial basis function (RBF) network which is trained on-line to adapt system uncertainties. The complete IPM motor drive is simulated in Matlab/Simulink environment.