An iterative learning control approach for two dimensional discrete Fornasini-Mrchesini Model

Almost all existing Iterative Learning Control (ILC) algorithms have focused on One-Dimensional (1-D) dynamical systems, and seldom are used in multidimensional systems. In this article, a two-gain ILC law is presented to deal with ILC issue of Two-Dimensional (2-D) linear discrete systems described by the First Fornasini-Marchesini Model (FMMI). Convergence of the proposed ILC law under identical boundary condition is theoretically investigated. A super-vector technique is used to transfer the ILC process of 2-D FMMI into a 2-D Roessor model such that a sufficient convergence condition of the proposed ILC law is derived. Also, robustness of the proposed ILC law against iteration-variant boundary condition is illustrated by simulation.