开关磁阻电机神经网络自适应PWM转速控制

Neural Network Based Adaptive PWM Speed Control in Switched Reluctance Motors

开关磁阻电机调速系统(SRD)作为1种交流无级调速系统以其宽广的调速范围和优越的调速性能而倍受关注。但由于开关磁阻电机高度的非线性和多变量的特点,很难建立其精确的数学模型,使得SRD的控制存在较大难度。针对这一问题,提出1种基于径向基函数(radial basis function, RBF)神经网络的开关磁阻电机自适应PWM转速控制方法。该方法利用RBF神经网络极强的逼近能力和快速的收敛性,将离线训练好的网络构成转速控制器,并结合网络的在线训练,让控制器在电机运行中自适应地调节网络参数,使之适应环境的变化。同时构造另一个RBF网络对控制对象进行在线辨识,为控制网络的在线学习提供所需的梯度参数。通过实验,证明了该方法具有响应速度快、控制精度高、适应性强等优点。

The switched reluctance motor drive（SRD） has obtained great attention as an AC stepless speed control system due to its large scale regulating scope, low cost and ruggedness. However, its strong nonlinearity and multivariable characteristic make it difficult to control. To solve this problem, this paper presents an approach of adaptive pulse width modulation（PWM） speed control for switched reluctance motors based on RBF neural network. This method builds up a speed controller based on RBF neural network which has powerful approximating ability and fast convergence property. After being trained off-line, the speed controller regulates network＇s parameters at running to adapt the environment under the training on-line. In addition, another RBF network is constructed to offer gradient parameters, which is needed by the on-line training, via on-line identification. The results of experiments prove that the approach has lots of advantages in response speed, control accuracy and adaptability.