Torque ripple minimization in non-sinusoidal synchronous reluctance motors based on artificial neural networks

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• 作者：Phuoc Hoa Truong^a ; ^{phuoc-hoa.truong@uha.fr" class="auth_mail" title="E-mail the corresponding author} ; Damien Flieller^b ; ^{damien.flieller@insa-strasbourg.fr" class="auth_mail" title="E-mail the corresponding author} ; Ngac Ky Nguyen^c ; ^{ngacky.nguyen@ensam.eu" class="auth_mail" title="E-mail the corresponding author} ; Jean Mercklé ; ^a ; ^{jean.merckle@uha.fr" class="auth_mail" title="E-mail the corresponding author} ; Guy Sturtzer^d ; ^{guy.sturtzer@insa-strasbourg.fr" class="auth_mail" title="E-mail the corresponding author}
• 关键词：Non-sinusoidal synchronous reluctance motor ; Torque ripple ; Optimal currents ; Lagrange optimization ; Adaline ; Artificial neural networks
• 刊名：Electric Power Systems Research
• 出版年：2016
• 出版时间：November 2016
• 年：2016
• 卷：140
• 期：Complete
• 页码：37-45
• 全文大小：4549 K

文摘

This paper proposes a new method based on artificial neural networks for reducing the torque ripple in a non-sinusoidal synchronous reluctance motor. The Lagrange optimization method is used to solve the problem of calculating optimal currents in the d–q frame. A neural control scheme is then proposed as an adaptive solution to derive the optimal stator currents giving a constant electromagnetic torque and minimizing the ohmic losses. Thanks to the online learning capacity of neural networks, the optimal currents can be obtained online in real time. With this neural control, each machine's parameter estimation errors and current controller errors can be compensated. Simulation and experimental results are presented which confirm the validity of the proposed method.