ANN-based position and speed sensorless estimation for BLDC motors
This addresses the need for precise motor control without sensors in industrial applications, but it is incremental as it builds on existing ANN methods with mixed improvements over advanced techniques.
The paper tackles sensorless estimation of position and speed for BLDC motors using ANNs trained on voltage data, achieving absolute errors of 0.8 electrical degrees for position and 22 rpm for speed in tests from 125 to 1,500 rpm.
BLDC motor applications require precise position and speed measurements, traditionally obtained with sensors. This article presents a method for estimating those measurements without position sensors using terminal phase voltages with attenuated spurious, acquired with a FPGA that also operates a PWM-controlled inverter. Voltages are labelled with electrical and virtual rotor states using an encoder that provides training and testing data for two three-layer ANNs with perceptron-based cascade topology. The first ANN estimates the position from features of voltages with incremental timestamps, and the second ANN estimates the speed from features of position differentials considering timestamps in an acquisition window. Sensor-based training and sensorless testing at 125 to 1,500 rpm with a loaded 8-pole-pair motor obtained absolute errors of 0.8 electrical degrees and 22 rpm. Results conclude that the overall position estimation significantly improved conventional and advanced methods, and the speed estimation slightly improved conventional methods, but was worse than in advanced ones.