On Convergence, Tracking-performance and Task-flexibility of Joint Parametrized/Signal-based Iterative Learning Control
Many industrial motion systems require performing a variety of tasks with high precision and safety. Iterative learning control (ILC) is a method with convergent update laws, generally classified into: 1) parametrized learning approach for achieving task-flexibility against varying tasks; or 2) signal-based learning approach which can achieve perfect tracking-performance for repeating tasks. The aim of this study is to join the distinct ILC frameworks, achieving all desirable properties in a single framework. Specifications on convergence, tracking-performance and task-flexibility of the developed joint parametrized/signal-based ILC are theoretically derived, confirmed with experimental results on a two-mass system.
Authors
- Kentaro Tsurumoto (ORCID: https://orcid.org/0000-0002-0609-201X)
- Wataru Ohnishi (ORCID: https://orcid.org/0000-0002-4221-9305)
- Maurice Poot (ORCID: https://orcid.org/0000-0001-5225-4123)
- Tom Oomen (ORCID: https://orcid.org/0000-0001-7721-4566)
- Takafumi Koseki (ORCID: https://orcid.org/0000-0003-3922-0652)
- Johan Kon (ORCID: https://orcid.org/0009-0007-3553-8934)
Institutions
- Tokyo University of Information Sciences (JP)
- The University of Tokyo (JP)
- Eindhoven University of Technology (NL)
- Delft University of Technology (NL)
Publication Details
- Journal
- IEEJ Journal of Industry Applications
- Published
- 2025-12-25
- DOI
- https://doi.org/10.1541/ieejjia.20250046
- Primary Topic
- Iterative Learning Control Systems
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- Japan Society for the Promotion of Science