Iterative learning control for region to region tracking

Iterative learning control (ILC) is a high performance control design method for systems working in a repetitive manner by learning from previous experience. Most existing ILC design considers the problem where the desired reference trajectory is defined either fully on the trial duration or on a finite number of intermediate time instants. This paper further expands the applicability of ILC by studying a more general case (named region to region tracking) where there is no desired trajectory defined at all; instead only a region where the system output should reach is given. To solve this problem, a novel ILC algorithm with an norm optimal ILC step and a projection operation is developed. Convergence properties of the algorithm are analysed rigorously. It is also shown that traditional reference tracking problems can be solved as special cases of the proposed design, resulting in a well-known norm optimal ILC algorithm being recovered and a new point to point ILC algorithm. Numerical simulations are presented to demonstrate the effectiveness of the proposed approach