Non-iterative control-oriented regularization for linear system identification

In the context of regularization methods for linear system identification, we introduce a new kernel design procedure that accounts for control objectives. We consider a model-reference control setup and assume data from one experiment is available. Exploiting the frequency response of the reference model, we design a new kernel that is able to extract the least amount of information from the data to the purpose of matching the desired closed-loop, with particular attention to user-defined frequency bands. Unlike the recently proposed CoRe algorithm, the proposed method is non-iterative and does not require any preliminary controller estimation. Simulation results on a benchmark example show that, when the model is used for control design, the proposed regularization procedure outperforms traditional kernel-based techniques as well as bias-shaping through data prefiltering.