An internal-model-based framework for the analysis and design of repetitive and learning controllers

Abstract

Repetitive and iterative learning control are two modern control strategies, used in tracking systems in which the signals are periodic in nature. Both schemes are in some sense based on the internal model principle applied to periodic signals. Because of the great number of successful applications, both schemes have been investigated in their own right, largely independent of the results available for the internal model principle. This has clouded some basic properties of these schemes, with consequences for their analysis and design. This paper returns to the origin of repetitive and learning control by analysing and designing both controllers within a general internal model based framework. A link is made with several existing approaches, for which the design is shown to depend on modifications of the internal model.