Performance Optimization of Switched Reluctance Motor Based on Water-cycle Algorithm

To address the problem in which a double-salient structure and nonlinear magnetic circuit saturation cause torque ripple and poor performance in the switched reluctance motor (SRM), we present a combined strategy of speed control and torque control to optimize its performance. First, we design a controller based on the water-cycle algorithm (WCA) to optimize the weights of the wavelet neural network (WNN). Based on the proposed controller, we then design a speed controller to make online adjustments of errors caused by unknown variation and external load disturbance. Next, we use an improved direct instantaneous torque control (DITC) to minimize the torque ripple in the inner loop. Finally, we built a model in Simulink and compared its performance with those of other methods, and then built a motor model in Maxwell based on the actual motor parameters. Based on a joint simuation of Simulink and Maxwell, we further verify the effectiveness of this method. The results show that the proposed method can effectively adapt to the external load disturbance and has strong robustness.