ANN-based position and speed sensorless estimation for BLDC motors
Fuente:
arXiv
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| Autori principali: | , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866916116383662080 |
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| author | Gamazo-Real, Jose-Carlos Martinez-Martinez, Victor Gomez-Gil, Jaime |
| author_facet | Gamazo-Real, Jose-Carlos Martinez-Martinez, Victor Gomez-Gil, Jaime |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2402_03534 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | ANN-based position and speed sensorless estimation for BLDC motors Gamazo-Real, Jose-Carlos Martinez-Martinez, Victor Gomez-Gil, Jaime Systems and Control Hardware Architecture Machine Learning 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. |
| title | ANN-based position and speed sensorless estimation for BLDC motors |
| topic | Systems and Control Hardware Architecture Machine Learning |
| url | https://arxiv.org/abs/2402.03534 |