Combined Time-Domain Optimization Design for Task-Flexible and High Performance ILC
Kentaro Tsurumoto (University of Tokyo)
Wataru Ohnishi (University of Tokyo)
Takafumi Koseki (University of Tokyo)
Max Van Haren (Eindhoven University of Technology)
T.A.E. Oomen (TU Delft - Team Jan-Willem van Wingerden, Eindhoven University of Technology)
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Abstract
Iterative learning control (ILC) yields substantial performance improvement for repetitive motion tasks. While task-flexibility for non-repetitive motion tasks can be achieved with the use of basis functions, this typically comes with a trade-off in performance or design parameters. This study aims to achieve both task-flexibility and high performance with a single time-domain optimization framework. By defining a criterion combining the cost for performance and task-flexibility, an optimal feedforward with task-flexibility of basis function ILC and high performance surpassing standard norm-optimal ILC is obtained. Numerical validation on a two-mass motion system confirm the capabilities of the developed framework.