A neural network-based commutation optimization strategy and drive system design for brushless DC mo

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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 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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