CoRe: Control-oriented Regularization for System Identification

In this paper, we develop a novel theoretical framework for control-oriented identification, based on a Bayesian perspective on modeling. Specifically, we show that closed-loop specifications can be incorporated within the identification procedure as a prior of the model probability distribution via suitable regularization. The effectiveness of the proposed strategy against state-of-the-art regularized identificaton is illustrated on a benchmark example for digital control system design.