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Aghashabani M, Milimonfared J, Kashefi Kaviani A, Ashabani M. A Neural Network-PSO Based Control for Brushless DC Motors for Minimizing Commutation Torque Ripple. Journal of Iranian Association of Electrical and Electronics Engineers. 2010; 7 (2) :15-22
URL: http://jiaeee.com/article-1-210-en.html
Abstract:   (1787 Views)

This paper presents the method of reducing torque ripple of brushless DC (BLDC) motor. The commutation
torque ripple is reduced by control of the DC link voltage during the commutation time. The magnitude of
voltage and commutation time is estimated by a neural network and optimized with an optimization method
named particle swarm optimization (PSO) algorithm analysis. The goal of optimization is to minimize the error
between the command torque and real torque and doesn’t need knowledge of the conduction interval of the three
phases. It adaptively adjusts the DC link voltage in commutation duration so that commutation torque ripple is
effectively reduced. In this paper, the performance of the proposed brushless DC (BLDC) control is compared
with that of conventional BLDC drives without input voltage control.

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Type of Article: Research | Subject: Power
Received: 2017/02/21 | Accepted: 2017/02/21 | Published: 2017/02/21

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