Improved Iterative Learning Direct Torque Control for Torque Ripple Minimization of Surface-Mounted Permanent Magnet Synchronous Motor Drives
Sadeq Ali Qasem Mohammed, Han Ho Choi, Jin‐Woo Jung
Abstract
This article presents an improved iterative learning direct torque control (IL-DTC) to remarkably minimize the torque ripples for a surface-mounted permanent magnet synchronous motor (SPMSM) drive. Unlike the conventional IL-DTC, the proposed IL-DTC significantly attenuates the torque ripples by effectively suppressing the repetitive disturbances using the speed and load torque compensating terms in the improved error dynamics via the improved feedback control terms and iterative learning control terms. Further, it has a simple structure and fast dynamic response due to the direct control of the torque and flux. The stability is verified through the convergence of speed errors to zero as the iteration index goes to infinity. The comparative results via MATLAB/Simulink and a prototype SPMSM test-bed with TI-TMS320F28335-DSP demonstrate the improved control performance (e.g., less torque ripples, faster transient response, smaller overshoot/undershoot, and smaller steady-state error) over the conventional IL-DTC under critical load/speed conditions with severe model parameter uncertainties.