A current cycle feedback iterative learning control approach for AFM imaging

Ying Wu, Qingze Zou, Chanmin Su

Research output: Contribution to journalArticlepeer-review

32 Scopus citations


In this paper, we proposed a novel current cycle feedback (CCF) iterative learning control (ILC) approach to achieve high-speed imaging on atomic force microscope (AFM). AFM imaging requires precision positioning of the AFM probe relative to the sample in 3-D ($x$ $y$$ z$). It has been demonstrated that, with advanced control techniques such as the inversion-based iterative control (IIC), precision positioning of the AFM probe in the lateral ($x$ $y$) scanning can be successfully achieved. Precision positioning of the probe in the vertical $z$-axis direction, however, is still challenging because the issues such as the sample topography are unknown, in general; the probesample interaction is complicated, and the probesample position is sensitive to the probesample interaction. The main contribution of this paper is the development of the CCF-ILC approach for the AFM $z$-axis control, which decouples the robustness of the feedback control from the precision tracking of the feedforward control. Particularly, the proposed CCF-ILC controller design utilizes the recently developed robust inversion technique to minimize the model uncertainty effect on the feedforward control and to remove the causality constraints in other CCF-ILC approaches. It is shown that the iterative law converges and attains a bounded tracking error upon noise and disturbances. The proposed method is illustrated through experimental implementation, and the experimental results show an increase of eight times faster imaging speed for contact-mode imaging.

Original languageEnglish (US)
Article number4781804
Pages (from-to)515-527
Number of pages13
JournalIEEE Transactions on Nanotechnology
Issue number4
StatePublished - Jul 2009
Externally publishedYes

All Science Journal Classification (ASJC) codes

  • Computer Science Applications
  • Electrical and Electronic Engineering


  • Atomic force microscope (AFM)
  • Inversion-based feedforward control
  • Iterative learning control (ILC)
  • Nanotechnology
  • Piezoelectric materials


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