Title :

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

Author(s) :

Flieller, Damien [Auteur]
Groupe de Recherche en Electrotechnique et Electronique de Nancy [GREEN]
Nguyen, Ngac-Ky [Auteur]
13338|||Laboratoire d’Électrotechnique et d’Électronique de Puissance - ULR 2697 [L2EP]
Wira, Patrice [Auteur]
Modélisation, Intelligence, Processus et Système [MIPS]
Sturtzer, Guy [Auteur]
Groupe de Recherche en Electrotechnique et Electronique de Nancy [GREEN]
Abdeslam, Djaffar Ould [Auteur]
Modélisation, Intelligence, Processus et Système [MIPS]
Mercklé, Jean [Auteur]
Modélisation, Intelligence, Processus et Système [MIPS]

Journal title :

IEEE Transactions on Industrial Electronics

Volume number :

61

Pages :

655-666

Publisher :

Institute of Electrical and Electronics Engineers

Publication date :

2014-02

ISSN :

0278-0046

English keyword(s) :

Adaline
cogging torque
homopolar current
neurocontroller
permanent-magnet synchronous motor (PMSM)
torque ripple

HAL domain(s) :

Sciences de l'ingénieur [physics]/Traitement du signal et de l'image [eess.SP]
Informatique [cs]/Traitement du signal et de l'image [eess.SP]

English abstract : [en]

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 ...
Show more >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.Show less >

Language :

Anglais

Peer reviewed article :

Oui

Audience :

Internationale

Popular science :

Non

Comment :

https://hal.archives-ouvertes.fr/hal-01019390v1

Collections :

• Laboratoire d'Électrotechnique et d'Électronique de Puissance (L2EP) - ULR 2697

Research team(s) :

Équipe Commande

Submission date :

2020-05-15T14:18:25Z
2022-02-18T09:44:13Z