A Self-Learning Solution for Torque Ripple Reduction for Nonsinusoidal Permanent-Magnet Motor Drives Based on Artificial Neural Networks - Archive ouverte HAL Accéder directement au contenu
Article Dans Une Revue IEEE Transactions on Industrial Electronics Année : 2014

A Self-Learning Solution for Torque Ripple Reduction for Nonsinusoidal Permanent-Magnet Motor Drives Based on Artificial Neural Networks

Résumé

This paper presents an original method, based on artificial neural networks, to reduce the torque ripple in a permanent-magnet nonsinusoidal synchronous motor. Solutions for calculating optimal currents are deduced from geometrical considerations and without a calculation step, which is generally based on the Lagrange optimization. These optimal currents are obtained from two hyperplanes. This paper takes into account the presence of harmonics in the back-EMF and the cogging torque. New control schemes are thus proposed to derive the optimal stator currents giving exactly the desired electromagnetic torque (or speed) and minimizing the ohmic losses. The torque and the speed control scheme both integrate two neural blocks, one dedicated for optimal-current calculation and the other to ensure the generation of these currents via a voltage source inverter. Simulation and experimental results from a laboratory prototype are shown to confirm the validity of the proposed neural approach.
Fichier principal
Vignette du fichier
MIPS_2014_06_MANUSCRIT.pdf (3.37 Mo) Télécharger le fichier
Origine : Fichiers produits par l'(les) auteur(s)
Loading...

Dates et versions

hal-01019390 , version 1 (25-08-2014)

Identifiants

Citer

Damien Flieller, Ngac Ky Nguyen, Patrice Wira, Guy Sturtzer, Djaffar Ould Abdeslam, et al.. A Self-Learning Solution for Torque Ripple Reduction for Nonsinusoidal Permanent-Magnet Motor Drives Based on Artificial Neural Networks. IEEE Transactions on Industrial Electronics, 2014, 61 (2), pp.655-666. ⟨10.1109/TIE.2013.2257136⟩. ⟨hal-01019390⟩
196 Consultations
464 Téléchargements

Altmetric

Partager

Gmail Facebook X LinkedIn More