摘要：AbstractMany data-driven control design methods require thea-prioriselection 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 ahyperparameterto be tuned. The proposed approach allows one to jointly optimize the reference model and learn an LPV controller, solely based onsoftspecifications 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.
