On Convergence, Tracking-performance and Task-flexibility of Joint Parametrized/Signal-based Iterative Learning Control
Kentaro Tsurumoto (University of Tokyo)
Wataru Ohnishi (University of Tokyo)
Takafumi Koseki (University of Tokyo)
Johan Kon (Eindhoven University of Technology)
Maurice Poot (Eindhoven University of Technology)
Tom Oomen (TU Delft - Team Jan-Willem van Wingerden, Eindhoven University of Technology)
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Abstract
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.