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Appl. Sci. 2016, 6(9), 254; doi:10.3390/app6090254

Fourier Series Learning Control for Torque Ripple Minimization in Permanent Magnet Synchronous Motors

1
División de Investigación y Posgrado, Facultad de Ingeniería, Universidad Autónoma de Querétaro, Cerro de las Campanas s/n, C.P. 76010, Santiago de Querétaro, Querétaro, Mexico
2
Tecnologico de Monterrey, Campus Querétaro, Avenue Epigmenio González #500, Fracc. San Pablo, C.P. 76130, Santiago de Querétaro, Querétaro, Mexico
*
Author to whom correspondence should be addressed.
Academic Editor: Chien-Hung Liu
Received: 7 July 2016 / Revised: 26 August 2016 / Accepted: 29 August 2016 / Published: 13 September 2016
View Full-Text   |   Download PDF [2363 KB, uploaded 13 September 2016]   |  

Abstract

A new Fourier Series Learning Controller (FSLC) for velocity control on a Permanent Magnet Synchronous Motor (PMSM) is proposed and implemented. An analysis of the error convergence for the FSLC is presented, and the update law for the Fourier series coefficients is specified. The field-oriented control method is used as a basic element to implement three different controllers for a PMSM. The performance of the FSLC is compared with two control methods, a classical PI (Proportional Integral) controller and an artificial neural network controller. The periodic nature of torque ripple in PMSMs is considered as a periodic disturbance, which must be compensated by the controller. With the FSLC implementation, a substantial reduction of the velocity ripple is obtained. Furthermore, a higher speed of learning is achieved with the FSLC in comparison with the artificial neural network. View Full-Text
Keywords: fourier series; torque ripple; PMSM; learning controller; field-oriented control fourier series; torque ripple; PMSM; learning controller; field-oriented control
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MDPI and ACS Style

Espíndola-López, E.; Gómez-Espinosa, A.; Carrillo-Serrano, R.V.; Jáuregui-Correa, J.C. Fourier Series Learning Control for Torque Ripple Minimization in Permanent Magnet Synchronous Motors. Appl. Sci. 2016, 6, 254.

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