Abstract
This paper is devoted to establishing a novel data-based optimization control framework of achieving the perfect tracking performances for learning systems in the absence of any model information. A modified Willems’ fundamental lemma is developed without the assumptions on the controllability and the input’s persistency of excitation, under which a data-based iterative learning control (ILC) framework is further presented. It is shown that the proposed data-based ILC can be employed to realize the perfect tracking tasks without any explicit model knowledge. A simulation example is provided to demonstrate the effectiveness of the proposed data-based ILC strategy.
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