An investigation of Adaline for torque ripple minimization in Non-Sinusoidal Synchronous Reluctance Motors
| Autor: | Phuoc Hoa Truong, Jean Merckle, Guy Sturtzer, Damien Flieller, Ngac Ky Nguyen |
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| Přispěvatelé: | Modélisation, Intelligence, Processus et Système (MIPS), Université de Haute-Alsace (UHA) Mulhouse - Colmar (Université de Haute-Alsace (UHA)), Groupe de Recherche en Electrotechnique et Electronique de Nancy (GREEN), Université de Lorraine (UL), Laboratoire d’Électrotechnique et d’Électronique de Puissance - ULR 2697 (L2EP), Centrale Lille-Université de Lille-Arts et Métiers Sciences et Technologies, HESAM Université - Communauté d'universités et d'établissements Hautes écoles Sorbonne Arts et métiers université (HESAM)-HESAM Université - Communauté d'universités et d'établissements Hautes écoles Sorbonne Arts et métiers université (HESAM)-JUNIA (JUNIA), Université catholique de Lille (UCL)-Université catholique de Lille (UCL), Laboratoire de Génie de la Conception (LGeco), Institut National des Sciences Appliquées - Strasbourg (INSA Strasbourg), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA), Ecole Nationale Supérieure d'Ingénieur Sud Alsace-Université de Haute-Alsace (UHA) Mulhouse - Colmar (Université de Haute-Alsace (UHA))-IUT de Colmar-IUT de Mulhouse, Centrale Lille-Haute Etude d'Ingénieurs-Université de Lille-Arts et Métiers Sciences et Technologies, HESAM Université (HESAM)-HESAM Université (HESAM) |
| Rok vydání: | 2013 |
| Předmět: |
Engineering
Stator 020209 energy 02 engineering and technology law.invention Reluctance motor Control theory law 0202 electrical engineering electronic engineering information engineering Torque ripple [SPI.NANO]Engineering Sciences [physics]/Micro and nanotechnologies/Microelectronics Optimized production technology Machine control Reluctance motors Artificial neural network business.industry Stators 020208 electrical & electronic engineering Electromagnetics Control engineering Optimal control Switched reluctance motor Torque Direct torque control business Neural networks |
| Zdroj: | IECON Proceeding de IECON 2013 Industrial Electronics Society, IECON 2013-39th Annual Conference of the IEEE (2013-11-10 to 2013-11-13 : Vienna, Austria) Industrial Electronics Society, IECON 2013-39th Annual Conference of the IEEE (2013-11-10 to 2013-11-13 : Vienna, Austria), Nov 2013, Vienne, France. pp.2602-2607, ⟨10.1109/IECON.2013.6699541⟩ |
| DOI: | 10.1109/iecon.2013.6699541 |
| Popis: | International audience; This paper presents a new method based on Artificial Neural Networks to obtain the optimal currents, for reducing the torque ripple in a Non-sinusoidal Synchronous Reluctance Motor. Optimal current control has to develop a constant electromagnetic torque and minimize the ohmic losses. In d-q reference frame without homopolar current, the direct and quadrature optimal currents will be determined thanks to Lagrange optimization. A neural control scheme is then proposed as an adaptive solution to derive the optimal stator currents. Thanks to learning capacity of neural networks, the optimal currents will be obtained online. With this neural control, either machine's parameters estimation errors or current controller errors can be compensated. Simulation results using Matlab/Simulink are presented to confirm the validity of the proposed method. |
| Databáze: | OpenAIRE |
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