摘要
An optimized commutation method based on backpropagation(BP)neural network is proposed to resolve the low stability and high-power consumption caused by inaccurate commutation point prediction in conventional commutation strategy during acceleration and deceleration.This article also builds a complete brushless DC motor drive system based on the GD32F103 micro control unit(MCU),with an Artix-7 XC7A35T field programmable gate array(FPGA)to meet the performance requirements of neural network calculation for real-time motor commutation control.Experimental results show that the proposed optimization strategy can effectively improve the system stability during system acceleration and deceleration,and reduce the current spikes generated during speed chan-ges.The system power consumption is reduced by about 11.7%on average.
基金
the National Key Research and Development Program(No.2017YFB0406204,2016YFC0105604)
Beijing Science and Technology Projects(No.Z181100003818002)
Science and Technology Service Network Initiative(No.FJ-STS-QYZX-099,KFJ-STS-ZDTP-069).
