Control-oriented regularization for linear 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 corresponding kernel varies according to the additional penalty term and provides a new insight on control-oriented identification. As a secondary contribution, we derive a Bayesian robust control design approach exploiting all the information coming from the above modeling procedure, including the estimate of the uncertainty set The effectiveness of the proposed strategy against state-of-the-art regularized identification is illustrated on a benchmark example for digital control system design. (C) 2021 Elsevier Ltd. All rights reserved.

Automated summary

This paper presents a novel theoretical framework for control-oriented identification based on a Bayesian modeling perspective, emphasizing the incorporation of closed-loop specifications through suitable regularization. Additionally, a Bayesian robust control design approach is discussed, utilizing all information from the modeling procedure and demonstrating its effectiveness against state-of-the-art regularized identification in digital control system design.