ANN-based position and speed sensorless estimation for BLDC motors
Jose-Carlos Gamazo-Real Victor Martinez-Martinez Jaime Gomez-Gil
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.