Gradient Estimator-Based Amplitude Estimation for Dynamic Mode Atomic Force Microscopy: Small-Signal Modeling and Tuning

Abstract

Atomic force microscopy (AFM) plays an important role in nanoscale imaging application. AFM works by oscillating a microcantilever on the surface of the sample being scanned. In this process, estimating the amplitude of the cantilever deflection signal plays an important role in characterizing the topography of the surface. Existing approaches on this topic either have slow dynamic response e.g., lock-in-amplifier or high computational complexity e.g., Kalman filter. In this context, gradient estimator can be considered as a trade-off between fast dynamic response and high computational complexity. However, no constructive tuning rule is available in the literature for gradient estimator. In this paper, we consider small-signal modeling and tuning of gradient estimator. The proposed approach greatly simplifies the tuning procedure. Numerical simulation and experimental results are provided to demonstrate the suitability of the proposed tuning procedure.

A Comparative Study of FLL and PLL in Boost PFC Converter Control for Smart Greenhouse Farming Application

“…As such, the FLL gain can be tuned as a function of the desired settling time, for a which the formula is available in the literature [26].…”

Proportional‐resonant based control strategy for grid‐connected packed‐E cell inverters with Lyapunov filter‐based PLL

“…To tune the proposed PLL, small‐signal modelling can be used. In [45], it has been found that the Lyapunov estimator tuning gain ζ can be tuned as: $$\zeta =8/t_{ss}$$, where $$t_{ss}$$ is the desired settling time. Then, applying the small‐signal PLL model given in [46] to the developed PLL in this work, the frequency estimation gain γ can be tuned as a function of the phase margin and/or settling time.…”

On Biased Harmonic Signal Estimation: Application to Electric Power Grid Monitoring

“…Many engineering applications require fast, accurate, and online parameter estimation of a biased harmonic signal. These parameters are required to ensure maximum power transfer through gridconnected converters (GCCs) [1], [2], reject harmonic disturbance in a DC motor [3], enhance ride comfort through semi-active suspension system [4], monitor the status of a power grid [5], reconstruct the material surface topology by atomic force microscopy [6], estimate the rotational speed of turbocharger [7], measure the wheel speed through a vibration signal [8], to name a few. These applications require fast convergent algorithms that are not computationally demanding, easy to implement, and robust to measurement noise and disturbances.…”