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AFM imaging requires precision positioning of the AFM probe relative to the sample in all x-y-z axes, especially the vertical z-axis direction. Recently, the current-cycle-feedback iterative-learning-control (CCF-ILC) approach is proposed for high-speed AFM imaging. The CCF-ILC feedforward-feedback 2 degree-of-freedom (DOF) controller design has been successfully implemented for iteratively imaging on one scanline. In this article, we extend this CCF-ILC approach to the entire imaging of samples. The main contribution of this article is the analysis and the use of the CCF-ILC approach for tracking sample profiles with variations between scanlines (called line-to-line sample variations). The convergence (stability) of the CCF-ILC system is analyzed for the general case where the line-to-line sample variation occurs at each iteration. The allowable line-to-line sample profile variation is quantified. The performance improvement of the CCF-ILC is discussed by comparing the tracking error of the CCF-ILC technique to that of using feedback control alone. The proposed CCF-ILC control approach is illustrated by implementing it to the z-axis direction control in AFM imaging. Experimental results show that the imaging speed can be significantly increased by using the proposed approach.