Optimization of an ANN-based speed and position estimator for an FOC-controlled PMSM using genetic algorithm

Juan Paolo Quismundo, Edwin Sybingco, Maria Antonette Roque, Alvin Chua, Leonard Ambata


To further improve the performance of sensorless permanent magnet synchronous motor (PMSM) implementations, this study develops a neural network-based estimator for the speed and position estimation of a PMSM using field oriented control (FOC) as its control scheme. The proposed neural network’s hyperparameters are optimized using genetic algorithm. The neural network is trained and optimized based on a training dataset obtained from the Simulink simulation of the motor control system. The hyperparameters optimized include the training algorithm parameters, batch size, and the number of hidden layers and the corresponding neurons. The proposed estimator performed with better estimation accuracy than conventional estimators such as the sliding mode observer (SMO), model reference adaptive system (MRAS), and two other neural network configurations. The qualifications were made on steady-state and dynamic conditions. In terms of efficiency, the proposed estimator has a relatively lower power consumption but still falls short of the power drawn when using an actual sensor. The qualification process verified that the optimization of the neural network’s hyperparameters using genetic algorithm can provide a better performance in the estimation of motor parameters in sensorless motor applications.


artificial neural networks; genetic algorithm; PMSM; sensorless field oriented control;

Full Text:


DOI: http://doi.org/10.12928/telkomnika.v21i6.24511


  • There are currently no refbacks.

Creative Commons License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

TELKOMNIKA Telecommunication, Computing, Electronics and Control
ISSN: 1693-6930, e-ISSN: 2302-9293
Universitas Ahmad Dahlan, 4th Campus
Jl. Ringroad Selatan, Kragilan, Tamanan, Banguntapan, Bantul, Yogyakarta, Indonesia 55191
Phone: +62 (274) 563515, 511830, 379418, 371120
Fax: +62 274 564604