Synthesis Compensation Using Iterative Learning Control for Periodic Force Ripple of Permanent Magnet Linear Motor

A synthesis compensation approach of iterative learning control (ILC) is applied to minimization of periodic force ripples in a permanent magnet linear synchronous motor (PMLSM). Based on position, speed and time axes, the synthesis approach enables powerfully to compensate periodic force ripples along three axes with feedforward approach in PMLSMs, meanwhile stochastic disturbances are restrained by feedback approach. The ILC scheme with variable step-size is adopted to improve convergent rate along position. Three ILC approaches converge into compensation tables along position, speed and time axes respectively. In three ILC compensation schemes, an additional compensator combined with a conventional proportional-integral (PI) control is employed to generate a compensated reference current signal, which is added to the reference current from PI controller. As a consequence, the force pulsation is reduced significantly. The proposed approach is evaluated by experiments, which validate its effectiveness.