A design approach for noncausal robust Iterative Learning Control using worst case disturbance optimisation

In this paper, we present a novel iterative learning control (ILC) strategy that is robust against model uncertainty, as given by a system model and an additive uncertainty bound. The design methodology hinges on H infin optimisation, however, the procedure is modified such that the ILC controller is noncausal and inherently acts on a finite time interval. The resulting controller has the structure of a norm optimal ILC controller, so that robustness can be easily assessed. Furthermore, in an example, we show that the presented robust ILC controller can outperform linear quadratic ILC controllers.