Optimal Cost Function Parameter Design in Predictive Torque Control (PTC) Using Artificial Neural Networks (ANN)
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Fecha
2021-08
Profesor/a Guía
Facultad/escuela
Idioma
en_US
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Título del volumen
Editor
Institute of Electrical and Electronics Engineers Inc.
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Licencia CC
ATRIBUCIÓN 4.0 INTERNACIONAL
Licencia CC
https://creativecommons.org/licenses/by/4.0/deed.es
Resumen
The use of artificial neural networks (ANNs) for the selection of weighting factors in cost function of the finite-set model-predictive control (FS-MPC) algorithm can speed up selection without imposing additional computational burden to the algorithm and ensure that optimum weights are selected for the specific application. In this article, the ANN-based design process of the weighting factors is used for predictive torque control (PTC) in a motor drive. In the design process, the weighting factors in the cost function and the reference flux value are obtained using different fitness functions. The results show that different operating conditions of the drive will have new optimum parameters of the cost function; therefore, sweeping parameters like load torque or reference speed can optimize the PTC for the whole operating range of the drive. A good match of the performance metrics predicted by the ANN and the simulation model is also observed. The experiments demonstrate that the selected cost function parameters can provide a fast drive start and good performance during different loading conditions and also in reversing of the drive.
Notas
Indexación: Scopus.
Palabras clave
Artificial neural network (ANN), drives, model-predictive torque control, voltage source converter (VSC), weighting factor design
Citación
IEEE Transactions on Industrial Electronics Volume 68, Issue 8, Pages 7309 - 7319 August 2021 Article number 9145815
DOI
10.1109/TIE.2020.3009607