Control of Switched Reluctance Motors based on Improved BP Neural Networks

Background: Switched reluctance motors have a strong nonlinear performance due to their structure and operation mode. The performance and control strategy of this kind of motor are obviously different from those traditional strategies. As a result, the accurate model and high performance control of the switched reluctance motor prove to be very important and has obtained wide researches. Method: A kind of switched reluctance motor based on PID neural network control strategy is proposed, which combines artificial fish swarm and particle swarm optimization to optimize weights and thresholds of BP neural networks. Results: Speed responses of the improved BP algorithm have no overshoot, have a smooth transition to the steady state and eliminate the oscillation phenomena which is in the PID control. Conclusion: Besides, it reduces time of transient process to improve the response speed. Antiinterference ability and robustness are obviously superior to the PID control.