Direct frequency response function based, uncertainty accomodating optimal controller design

Here we present a new approach to optimal controller design which bridges the gap between system data and its complementary optimal controller. Starting with empirical, open-loop frequency response function (FRF) data, it is shown that the optimal controller can be derived directly without performing system identification. The primary benefit is that we are able to work directly with the measured data and the uncertainties inherent in it. This approach is viewed as advantageous because it has the ability to capture features in the model that a structured uncertainty model could not. Further, we go on to show a method of incorporating the empirical FRF uncertainty into the cost for robustness against plant uncertainty. This method leads to a more precise calculation of H₂ and LQG controllers since it avoids the residual errors associated with performing the traditional intermediary step of system identification, while concurrently accounting for measured system uncertainty.