Iterative learning control for uncertain systems : noncausal finite time-interval robust control design

In this paper, we present a novel robust Iterative Learning Control (ILC) control strategy that is robust against model uncertainty as given by an additive uncertainty model. The design methodology hinges on H_inf optimization, but formulated such that the obtained ILC controller is not restricted to be causal, and inherently operates on a finite time interval. Optimization of the robust ILC (R-ILC) solution is accomplished for the situation where any information about structure in the uncertainty is discarded, and for the situation where the information about the structure in the uncertainty is explicitly taken into account. Subsequently, the convergence and performance properties of resulting R-ILC controlled system are analyzed. On an experimental set-up, we show that the presented robust ILC control strategy can outperform an existing linear-quadratic norm-optimal ILC approach and an existing causal robust ILC approach based on frequency domain H_inf synthesis.

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