J.P. Yun, N.G. Kim, S.W. Kim, and T.J. Park (Korea)
PMSM, normalized iterative learning control, current com pensation, torque ripple minimization.
In permanent magnet synchronous motor (PMSM) con trol, it is known that periodic torque pulsations exist due to non-perfect sinusoidal flux distribution, cogging torque and current measurement errors. Because of the periodic na ture of torque pulsations, iterative learning control has re cently been chosen for torque ripple minimization. This pa per presents a normalized iterative learning control (NILC) scheme, a kind of variable-step-size algorithm in which step-size is varied adaptively; such an approach aims pri marily at faster convergence and reduction of steady-state error, with some degree of effectiveness. The performance of the proposed control scheme is evaluated through simu lation studies. The results show significant improvements in the steady-state torque response and the effectiveness in minimizing torque pulsation.
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