Open AccessData‐driven approach to iterative learning control via convex optimisation
Author(s) -
Nicoletti Achille,
Martino Michele,
Aguglia Davide
Publication year - 2020
Publication title -
iet control theory and applications
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.059
H-Index - 108
eISSN - 1751-8652
pISSN - 1751-8644
DOI - 10.1049/iet-cta.2018.6446
Subject(s) - iterative learning control , robustness (evolution) , control theory (sociology) , parametric statistics , computer science , convergence (economics) , regular polygon , robust control , iterative method , convex optimization , mathematical optimization , control engineering , control system , control (management) , engineering , mathematics , algorithm , artificial intelligence , biochemistry , chemistry , statistics , geometry , electrical engineering , economics , gene , economic growth
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.