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Abstract This paper presents a software-hardware integrated approach to high-speed large-range dynamic mode imaging of atomic force microscope (AFM). High speed AFM imaging is needed to interrogate dynamic processes at nanoscale such as cellular interactions and polymer crystallization process. High-speed dynamic-modes such as tapping-mode AFM imaging is challenging as the probe tapping motion is sensitive to the highly nonlinear probe-sample interaction during the imaging process. The existing hardware-based approach via bandwidth enlargement, however, results in a substantially reduction of imaging area that can be covered. Contrarily, control (algorithm)-based approach, for example, the recently developed adaptive multiloop mode (AMLM) technique, has demonstrated its efficacy in increasing the tapping-mode imaging speed without loss of imaging size. Further improvement, however, has been limited by the hardware bandwidth and online signal processing speed and computation complexity.Thus, in this paper, the AMLM technique is further enhanced to optimize the probe tapping regulation and integrated with a field programmable gate array (FPGA) platform to further increase the imaging speed without loss of imaging quality and range. Experimental implementation of the proposed approach demonstrates that the high-quality imaging can be achieved at a high-speed scanning rate of 100 Hz and higher, and over a large imaging area of over 20 µm.  

Abstract This paper is concerned with solving, from the learning-based decomposition control viewpoint, the problem of output tracking with nonperiodic tracking–transition switching. Such a nontraditional tracking problem occurs in applications where sessions for tracking a given desired trajectory are alternated with those for transiting the output with given boundary conditions. It is challenging to achieve precision tracking while maintaining smooth tracking–transition switching, as postswitching oscillations can be induced due to the mismatch of the boundary states at the switching instants, and the tracking performance can be limited by the nonminimum-phase (NMP) zeros of the system and effected by factors such as input constraints and external disturbances. Although recently an approach by combining the system-inversion with optimization techniques has been proposed to tackle these challenges, modeling of the system dynamics and complicated online computation are needed, and the controller obtained can be sensitive to model uncertainties. In this work, a learning-based decomposition control technique is developed to overcome these limitations. A dictionary of input–output bases is constructed offline a priori via data-driven iterative learning first. The input–output bases are used online to decompose the desired output in the tracking sessions and design an optimal desired transition trajectory with minimal transition time under input-amplitude constraint. Finally, the control input is synthesized based on the superpositioning principle and further optimized online to account for system variations and external disturbance. The proposed approach is illustrated through a nanopositioning control experiment on a piezoelectric actuator. 

In this article, a data‐driven difference‐inversion‐based iterative control (DDD‐IIC) approach is proposed to compensate for both nonlinear hysteresis and dynamics of Hammerstein systems. Simultaneous hysteresis‐dynamics compensation is needed in control of Hammerstein systems such as smart actuators, where effects of hysteresis and dynamics coexist and become pronounced in high‐speed, large‐range output tracking. Challenges, however, arises as hysteresis modeling, as needed in many existing control methods, can be complicated and prone to uncertainties, and the hysteresis and the dynamics are coupled and tend to change due to the variations of the system conditions (e.g., the aging of smart actuators). The proposed DDD‐IIC technique aims to achieve simultaneous hysteresis‐dynamics compensation with no need for modeling hysteresis and/or dynamics, and with both precision tracking and good robustness against hysteresis/dynamics variations. The convergence of the DDD‐IIC algorithm in the presence of random output disturbance/noise is analyzed. It is shown that when the noise is negligible, exact tracking is achieved and the size of hysteresis accounted is given by theGolden ratio. The proposed DDD‐IIC method is demonstrated via experiments of high‐speed large‐range output tracking on two different types of smart actuators with symmetric and asymmetric hysteresis behavior, respectively.