Optimal Cost Function Parameter Design in Predictive Torque Control (PTC) Using Artificial Neural Networks (ANN)

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dc.contributor.authorNovak, Mateja
dc.contributor.authorXie, Haotian
dc.contributor.authorDragicevic, Tomislav
dc.contributor.authorWang, Fengxiang
dc.contributor.authorRodriguez, Jose
dc.contributor.authorBlaabjerg, Frede
dc.date.accessioned2024-06-14T20:19:52Z
dc.date.available2024-06-14T20:19:52Z
dc.date.issued2021-08
dc.descriptionIndexación: Scopus.
dc.description.abstractThe 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.
dc.description.urihttps://ieeexplore-ieee-org.recursosbiblioteca.unab.cl/stamp/stamp.jsp?tp=&arnumber=9145815
dc.identifier.citationIEEE Transactions on Industrial Electronics Volume 68, Issue 8, Pages 7309 - 7319 August 2021 Article number 9145815
dc.identifier.doi10.1109/TIE.2020.3009607
dc.identifier.issn0278-0046
dc.identifier.urihttps://repositorio.unab.cl/handle/ria/57609
dc.language.isoen_US
dc.publisherInstitute of Electrical and Electronics Engineers Inc.
dc.rights.licenseATRIBUCIÓN 4.0 INTERNACIONAL
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/deed.es
dc.subjectArtificial neural network (ANN)
dc.subjectdrives
dc.subjectmodel-predictive torque control
dc.subjectvoltage source converter (VSC)
dc.subjectweighting factor design
dc.titleOptimal Cost Function Parameter Design in Predictive Torque Control (PTC) Using Artificial Neural Networks (ANN)
dc.typeAnimaAnimacióntion
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