Data‐driven approach to iterative learning control via convex optimisation. Issue 7 (31st March 2020)

• Record Type: Journal Article
• Authors: Nicoletti, Achille; Martino, Michele; Aguglia, Davide
• Is Part Of: IET control theory & applications. Volume 14:Issue 7(2020)
• Page Start: 972
• Page End: 981
• Publication Date: 2020-03-31
• Subjects: control system synthesis -- robust control -- learning systems -- optimisation -- adaptive control -- iterative methods -- nonlinear control systems -- convex programming -- frequency response; unmodelled dynamics -- low‐order parametric models -- convex optimisation problem -- learning filters -- frequency response data -- power converter control system -- data‐driven approach -- learning control methodology; Control theory -- Periodicals; Automatic control -- Periodicals; 629.8312
• DOI: 10.1049/iet-cta.2018.6446
• Languages: English
• ISSNs: 1751-8644

Abstract

Abstract : A new data‐driven iterative learning control methodology is presented which uses the frequency response data of a system in order to avoid the problem of unmodelled dynamics associated with low‐order parametric models. A convex optimisation problem is formulated to design the learning filters such that the convergence criterion is minimised. Since the frequency response data of the system is used in obtaining these filters, robustness is ensured by eliminating the uncertainty in the modelling process. The effectiveness of the method is illustrated by considering a case study where the proposed design scheme is applied to a power converter control system for a specific accelerator requirement at CERN.