Prefiltering in iterative feedback tuning: optimization of the prefilter for accuracy

Iterative feedback tuning (IFT) is a data-based method for the tuning of restricted complexity controllers. At each iteration, an update for the controller parameters is estimated from data obtained partly from the normal operation of the closed loop system and partly from a special experiment, in which the output signal obtained under normal operation is fed back at the reference input. The choice of a prefilter for the input data to the special experiment is a degree of freedom of the method. In this note, the prefilter is designed in order to enhance the accuracy of the IFT update. The optimal prefilter produces a covariance of the new controller parameter vector that is strictly smaller than the covariance obtained with the standard constant prefilter.