Adaptive Control of Discrete Repetitive Processes in Iteration Domain

Abstract

In this paper, the problem of model reference adaptive control for unit memory discrete repetitive processes is analyzed and solved by employing a lifting technique that allow us to view the discrete repetitive processes as a first-order multivariable plant. An adaptive controller gain adjustment algorithm in iteration domain is given that ensures the monotonic convergence of the tracking error between the process and the desired reference model outputs under persistent excitation conditions.