Abstract
This paper aims at presenting a data-based control design method for iterative learning control (ILC) systems such that the perfect tracking objective can be achieved without any model information. By only utilizing the input and output data collected in the test iterations, the trackability property of the given desired reference can be validated, which guarantees the existence of the desired input generating the desired reference for any ILC system with linear dynamics. Moreover, the idea of the observer design is leveraged to develop an ILC updating law only based on the collected input and output data. Thanks to the data-based ILC updating law, the perfect tracking objective is realized for ILC systems subject to any trackable desired reference despite the generally required full rank condition, where any knowledge of the model information is never needed.
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