Iterative Feedback Tuning (IFT) is a data based method for the tuning of restricted complexity controllers. At each iteration, an update for the parameters of the controller is estimated from data obtained partly from the normal operation of the closed loop system and partly from a special experiment. The choice of a prefilter for the input data to the special experiment is a degree of freedom of the method.
As a first contribution, the asymptotic convergence rate of IFT for disturbance rejection, which is one of the main fields of application, will be derived. Further, it will be investigated how to optimize the prefeilter with the objective of increasing the speed of convergence or of increasing the robustness of the method. It will be shown that these two objectives correspond to two different design criteria for the prefilter. However, in both cases the optimal prefilter can be computed from data collected under normal operating conditions of the plant.
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