【摘 要】
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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 pre-diction in conventional commutation strategy during acceleration an
【机 构】
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State Key Laboratory on Integrated Optoelectronics,Institute of Semiconductors,Chinese Academy of Sc
论文部分内容阅读
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 pre-diction 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 re-sults 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.
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