Control-oriented regularization for linear system identification. (May 2021)

• Record Type: Journal Article
• Authors: Formentin, Simone; Chiuso, Alessandro
• Is Part Of: Automatica. Volume 127(2021)
• Publication Date: 2021-05
• Subjects: Linear system identification -- Regularization -- Bayesian learning; Automatic control -- Periodicals; Automation -- Periodicals; 629.805
• DOI: 10.1016/j.automatica.2021.109539
• Languages: English
• ISSNs: 0005-1098

Abstract

Abstract: 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.