Torque–flux linkage recurrent neural network adaptive inversion control of torque for switched reluctance motor

In order to reduce the torque ripple for switched reluctance motor (SRM), the learning error preprocessing‐based torque–flux linkage recurrent neural network adaptive inversion control (TFRNNAIC) for SRM torque is proposed by the filter preprocessing and the non‐linear mechanism characteristics for SRM. In TFRNNAIC with the advantages of parallel and series–parallel structure, the torque feedback error learning method is employed to update the weights of the torque–flux linkage recurrent neural network. To suppress the ripple effectively in torque error used for the weight learning of the torque–flux linkage recurrent neural network and obtain an accurate TFRNNAIC, namely the torque–flux linkage model for SRM, the low‐pass filter preprocessing for the torque error is used. Moreover, the other low‐pass filter is executed to reduce the ripple in the output for the PD torque control. The superposition of outputs for TFRNNAIC and PD torque control is taken as the reference flux linkage. Compared with other control strategies, such as the classical parallel neural network control, the classical series–parallel neural network control and TFRNNAIC, the simulation results show that the learning error preprocessing‐based TFRNNAIC for SRM torque is capable of effectively reducing the torque ripple for SRM with the good recurrent performance.