A 2D Systems Approach to Iterative Learning Control for Discrete Linear Processes with Zero First Markov Parameter

In this paper we develop a new approach to iterative learning control for the practically relevant case of deterministic discrete linear plants where the first Markov parameter is zero. The basis for this is a 2D systems approach that, by using a strong form of stability for linear repetitive processes, also allows us to consider both trial-to-trial and along the trial performance. This is in contrast to many other approaches where the sole emphasis is on error convergence. The resulting design computations are in terms of Linear Matrix Inequalities (LMIs). Results from experimentally applying the resulting control law to one axis of a gantry robot are also given.