A position estimator for an outer rotor permanent magnet synchronous machine (PMSM) is presented and evaluated. This proposed estimator uses a machine-learning based neural network algorithm to interpret the signals, which are obtained from linear Hall-effect sensors located in the fringe field of the rotor. The main objective is to design a cost-effective position estimation system that is comparable to encoders and resolvers in functionality and performance, without the limitations of sensorless position estimation methods. Learning signal data sets are acquired with commercial sensors and an outer rotor PMSM, and offline training steps and results are discussed.
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Position Estimation of Outer Rotor PMSM Using Linear Hall Effect Sensors and Neural Networks
Semantic Scholar · Engineering · 2019
Abstract
A position estimator for an outer rotor permanent magnet synchronous machine (PMSM) is presented and evaluated. This proposed estimator uses a machine-learning based neural network algorithm to interpret the signals, which are obtained from linear Hall-effect sensors located in the fringe field of the rotor. The main objective is to design a cost-effective position estimation system that is comparable to encoders and resolvers in functionality and performance, without the limitations of sensorless position estimation methods. Learning signal data sets are acquired with commercial sensors and an outer rotor PMSM, and offline training steps and results are discussed.