Discrete-time adaptive iterative learning control for permanent magnet linear motor

Abstract

A discrete-time adaptive iterative learning control approach (DAILC) is presented for improving the permanent magnet linear motor velocity tracking performance. The learning gain can be updated iteratively along the learning axis and pointwisely along the time axis. When the initial states are random and the reference trajectory and disturbance are iteration-varying, the DAILC can achieve the pointwise convergence over a finite time interval asymptotically along the iterative learning axis. The theoretical analysis and simulation results further verify the effectiveness of the proposed approach.