Nonlinear Neural Network-based Modeling of Switched Reluctance Motor

In order to improve the performance of switched reluctance driving system, it is necessary to build an accurate switched reluctance motor (SRM) model. In this paper, a nonlinear flux-linkage model and a torque model of SRM are presented by using the measured accurate flux-linkage data, torque data and nonlinear mapping ability of BP neural network, which is based on fast self-configuring algorithm. In contrast with the traditional models, these two models have the abilities of fast convergence in training, good learning generalization, small network scale and easy real-time control. An experiment is carried out to demonstrate the accuracy and feasibility of the presented models. The result shows that the models have a better accuracy than the previous ones and are good for further optimization of the energy conversion and reducing the torque ripple.