Filtered-version iterative learning linear servo system with forgetting factor

An open-closed loop iterative learning controller was proposed to control the mover of a permanent magnet linear synchronous motor (PMLSM) servo system to track expectation linear position. The two-dimensional model of PMLSM and the convergence of the iterative learning linear servo system were analyzed in detail. The forgetting factor was optimized by reducing the trace of the input error covariance matrix. This factor is able to modify the iterative learning law of control input. The error signal of the feed-forward learning controller was filtered by a zero-phase FIR digital filter. Experiment results demonstrate that the filtered-version iterative learning controller with forgetting factor can surely improve the performance of the servo system in iterative learning process and effectively suppress the ripple of end force. The system has good learning convergence speed, dynamic response and control precision.