Abstract-The Atomic Force Microscope (AFM) is a powerful imaging and nanofabrication tool that allows the user to observe and manipulate samples at the atomic level. However, one limitation of current AFMs is the long time required to obtain a quality image of a sample. Several researchers have investigated this problem in recent years, and we give an overview of the approaches explored, including H∞, ℓ1, and model-inverse based methods. We compare and discuss advantages and disadvantages of the various approaches, and we end with a summary of open questions to be addressed in improving the control of AFMs.
We evaluate the performance of two control architectures applied to atomic force microscopes (AFM). Feedback-only control is a natural solution and has been applied widely. Expanding on that, combining feedback controllers with plant-injection feedforward filters has been shown to greatly improve tracking performance in AFMs. Alternatively, performance can also be improved by the use of a closed-loop-injection feedforward filter applied to the reference input before it enters the feedback loop. In this paper, we compare the plant-injection architecture with the closed-loop-injection architecture when used in controlling AFMs. In particular, we provide experimental results demonstrating the closed-loop-injection architecture yields better tracking performance of a raster scan.
Abstract-Noncollocated sensors and actuators, and/or fast sample rates with plants having high relative degree, can lead to nonminimum-phase (NMP) discrete-time zero dynamics that complicate the control system design. In this paper, we examine three stable approximate model-inverse feedforward control techniques, the nonmimimum-phase zeros ignore (NPZIgnore), the zero-phase-error tracking controller (ZPETC) and the zero-magnitude-error tracking controller (ZMETC), which have frequently been used for NMP systems. We analyze how the discrete-time NMP zero locations in the z-plane affect the success of the NPZ-Ignore, ZPETC, and ZMETC model-inverse techniques. We also provide simulation examples using plants based on the system identification of an atomic force microscope and a hard disk drive, showing the tradeoffs in performance relative to NMP zero locations in these different application systems.
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