Optimal preltering in Iterative Feedback Tuning

Iterative Feedback Tuning (IFT) is a data-based method for the iterative tuning of restricted complexity controllers. A \special experiment " in which a batch of previously collected output data is fed back at the reference input allows one to compute an unbiased estimate of the gradient of the control performance criterion. We show that, by performing an optimal ltering of the data that are fed back, one can minimize the asymptotic variability of the control performance cost, and hence minimize the average performance degradation that results from the randomness of the data. The expression of the optimal lter is derived, and a simulation illustrates the benets that result from using this optimal lter as compared to the use of the classical constant lter. Paper supported by the the Belgian Programme on Interuniversity Attraction Poles, initiated by the Belgian Federal Science Policy Oce and the European Research Network on System Identication (ERNSI) funded by the European Union. The scientic responsibility rests with its authors.