A data-driven approach to model-reference control with applications to particle accelerator power converters. (February 2019)

• Record Type: Journal Article
• Authors: Nicoletti, Achille; Martino, Michele; Karimi, Alireza
• Is Part Of: Control engineering practice. Volume 83(2019)
• Page Start: 11
• Page End: 20
• Publication Date: 2019-02
• Subjects: Convex optimization -- Data-driven control -- H1 control -- H2 control -- H∞ control -- Power converter control -- Robust control -- RST; Automatic control -- Periodicals; 629.89
• DOI: 10.1016/j.conengprac.2018.10.007
• Languages: English
• ISSNs: 0967-0661

Abstract

Abstract: A new model-reference data-driven approach is presented which uses the frequency response data of a system in order to avoid the problem of unmodeled dynamics associated with low-order parametric models. It is shown that a convex optimization problem can be formulated (in either the H ∞, H 2 or H 1 sense) to shape the closed-loop sensitivity functions while guaranteeing the closed-loop stability. The effectiveness of the method is illustrated by considering several case studies where the proposed design scheme is applied in both simulation and to a power converter control system for a specific accelerator requirement at CERN.