Abstract
As the demand for faster and more precise control has exponentially increased, the quality of feedforward (FF) control has become increasingly important. In this sense, iterative learning control (ILC) enables significant performance enhancements by learning the FF signal from previously repeated tasks. This study aims to develop a new framework that potentially creates a breakthrough in the current trade-off between the high performance and task flexibility of ILC. By combining a high-performance but non-flexible frequency-domain ILC (F-ILC) and a task-flexible and good (but not great) performance basis function ILC (B-ILC), both a task-flexible and high-performance ILC (C-ILC) is achieved. The proposed C-ILC framework was validated through a second-order system simulation, showing a task flexibility as high as that of B-ILC and a higher tracking performance then that of the current F-ILC framework.
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