Adaptive PWM speed control for switched reluctance motors based on RBF neural network

The switched reluctance motor drive (SRD) has obtained great attention as an AC stepless speed control system due to its large regulating scope, low cost and ruggedness. However, its strong nonlinearity and multivariable characteristic make it difficult to control. To solve the problem, this paper presents an approach of adaptive PWM speed control for switched reluctance motors (SRM) 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. The controller is trained off-line in advance, and then with the motor's operation, the on-line training of it makes its parameters vary with the environment in order to improve the control performance. 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.