A comparison of Inversion Based Iterative Learning Control Algorithms

The learning filter in Iterative Learning Control determines the performance in terms of convergence rate and converged error. The ideal learning filter is the inverse of the system being learned. For minimum phase system, direct system inversion can be implemented easily. However for non-minimum phase system, direct system inversion would result in an unstable filter. In the literature, there are several methods that approximate the system inversion. In time domain, zero-phase-error tracking controller (ZPETC) and zero-magnitude-error tracking controller (ZMETC) have been used frequently for non-minimum phase system. In frequency domain, Modelless Inversion-based Iterative Control (MIIC) has been used for atomic force microscope (AFM) imaging. In this paper, a data-based dynamic inversion method in the frequency domain is proposed, and the performance is compared with aforementioned inversion methods.