Research on switched reluctance motor control system based on fuzzy neural network PID

LU Zhuwei, HUANG Qixin

(School of mechanical and electrical engineering, Sanjiang University, Nanjing 210012, China)

Abstract: Aiming at the problem of torque ripple, large noise and speed instability of switched reluctance motor, the starting, running and adjusting speed of switched reluctance motor were studied, and a new approach was proposed based on fuzzy neural network PID control. Fuzzy control theory and BP neural network was combined to form a fuzzy BP neural network, which could adjust the parameters of PID according to the system error E, the change of the error of EC, and the change of the change of the error ECC. DSP was used as a control core and the asymmetric inverter bridge was used as a power converter, which could drive a 2 kW switched reluctance motor. The results indicate that this method can greatly improve the dynamic and static performance of switched reluctance motor control system. It has high control precision, small torque ripple, and high robustness to interference.

Key words: switched reluctance motor（SRM）; fuzzy control; neural network; proportion integration differentiation control