High-Performance Torque Control for Switched Reluctance Motor Based on Online Fuzzy Neural Network Modeling

A novel high performance torque control scheme for switched reluctance motors(SRMs) is proposed based on online fuzzy neural network modeling and adaptive sliding-mode current control. Firstly, an adaptive neural fuzzy inference system(ANFIS) is designed to learn the nonlinear static position-torque-current characteristic and the flux-linkage characteristic of an SRM offline. Then each phase torque is calculated according to torque share function and the desired phase current waveform obtained using the ANFIS inverse torque model. Considering the limitation of the offline model and the uncertainties existing in the real-time motor system, the parameters of ANFIS are tuned through online supervised learning to improve the accuracy of the inverse torque and the flux-linkage model. Based on the online flux-linkage model, an adaptive sliding-mode current controller is designed to regulate the actual SRM phase winding current to track the desired phase current waveform, thereby reduce the torque ripple of SRM.