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In this paper, we address a challenging and open problem: how to design a suitable iterative learning control (ILC) system in the presence of input singularity, which is incurred by the singularities of the system direct feed-through term. Considering two typical types of input singularities, we first revise the ILC operators accordingly by adding a forgetting factor and incorporating a time-varying learning gain, in the sequel guarantee ILC operators to be contractible. Next, using the Banach fixed-point theorem, we demonstrate that the output sequence can either enter and remains ultimately in a designated neighborhood of the target trajectory, or is bounded by a class K function. Finally, an illustrative example is presented.