A Practical Method to Speed-Up the Experimental Procedure of Iterative Learning Controllers

This paper proposes a practical approach for fastening the lengthy experimentational processes that may occur with iterative learning control (ILC) upto a certain level using simple low-order identified models. The traditional practice in ILC experiments is to update the ILC signal by directly using the experimental data after each run of the process which corresponds to one ILC update per one run. When considered from the point of experimental time, even conducting a moderate number of ILC updates can take quite long with this procedure. Since an accurate linear model can adequately represent the actual system upto a certain amplitude and/or frequency of the desired reference, we propose that the total experimental time can be reduced by updating the ILC signal via predicted system data until the limits of the linear model. This approach allows one to carry out large number of ILC updates while not needing to carry out the same amount of real experiments. Consequently, a significant number of experiments that would be needed for achieving the same results can be skipped with a simulation approach. The efficiency of the proposed method was tested through experimentation with three different UAV reference trajectories and the results demonstrated that it is possible to attain significant amount of tracking precision in several flight experiments.