Learning Speed Enhancement of Iterative Learning Control with Advanced Output Data based on Parameter Estimation

Abstract

Learning speed enhancement is one of the most important issues in learning control. If we can improve both learning speed and tracking performance, it will be helpful to the applicability of learning control. Considering these facts, in this paper, we propose a learning speed enhancement scheme for iterative learning control with advanced output data (ADILC) based on parameter estimation. We consider linear discrete-time non-minimum phase (NMP) systems, whose model is unknown, except for the relative degree and the number of NMP zeros. In each iteration, estimates of the impulse response are obtained from input-output relationship. Then, learning gain matrix is calculated from the estimates, and by using new learning gain matrix, learning speed can be enhanced. Simulation results show that the learning speed has been enhanced by applying the proposed method.