Model reference adaptive control of discrete repetitive processes in the iteration domain

In this paper we formulate and solve the problem of model reference adaptive control for unit memory discrete repetitive processes by employing a lifting technique that allows us to view the system as a first-order multivariable plant. An adaptive controller gain adjustment algorithm in the iteration domain is given that ensures convergence of the tracking error between the output of the process and the output of a given reference model when the plant and reference model are driven by the same input.