On data-driven design of LPV controllers with flexible reference models

Many data-driven control design methods require the a-priori selection of a reference model to be tracked. In case of limited priors on the plant, such a blind choice might ultimately compromise the overall performance. In this work, we propose a nested strategy for the direct design of Linear Parameter Varying (LPV) controllers from data, in which the reference model is treated as a hyperparameter to be tuned. The proposed approach allows one to jointly optimize the reference model and learn an LPV controller, solely based on soft specifications on the desired closed-loop. The effectiveness of the proposed technique is assessed on a benchmark case study, with the obtained results showing its potential advantages over a state-of-the-art method.