Torque Ripples Minimization in PMSM using Variable Step-Size Normalized Iterative Learning Control

Abstract

Periodic torque ripples exist due to non-perfect sinusoidal flux distribution, cogging torque and current measurement errors in permanent magnet synchronous motor (PMSM). These ripples are reflected as periodic oscillations in the motor speed and deteriorate the performance of application of PMSM as a high-precision tracking applications. In this paper, we propose a variable step-size normalized iterative learning control (VSS-NILC) scheme to reduce periodic torque ripples. VSS-NILC is combined to existing PI current controller and generates compensated reference current iteratively from cycle to cycle so as to minimize the mean square torque error. VSS-NILC scheme alters the step-size of the update equation to reduce the conflict between speed of convergence and minimum mean square error (MSE). Consequently VSS-NILC scheme has faster convergence rate and lower mean square torque error. Simulation results show significant improvements in the steady-state torque response and the effectiveness in minimizing torque ripples