Data-Driven Feedforward Hysteresis Compensation with Genetic Algorithm for Atomic Force Microscope*

Navid Asmari, Mustafa Kangül, Santiago H. Andany, A. Karimi, G. Fantner
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引用次数: 0

Abstract

Nonlinear dynamics of piezo actuators such as hysteresis, distort the Atomic Force Microscopy (AFM) images as they adversely affect the accuracy of the nano-positioning setup. To compensate for the effects of hysteresis on lateral scanner actuators of AFM, a data-driven feedforward controller design algorithm is proposed. The pair of forward and backward images of a sample are used to extract a mapping between the trace and retrace motion of the actuator. A model corresponding to the input-output mapping of the actuator is defined with a set of unknown parameters. The values of these parameters, which shape the hysteresis curves of the actuator, are optimized through defining and solving an optimization problem. A genetic algorithm is utilized as a tool to look for the optimal values. The hysteresis mapping model is then implemented in the form of an inversion-based feedforward controller to correct the scan waveforms and get matching forward and backward images of the sample. The proposed sensor-less data-driven method is easy to implement as it does not depend on the instrument, the sample under study, or the imaging properties.
基于遗传算法的原子力显微镜数据驱动前馈迟滞补偿*
压电致动器的非线性动力学,如迟滞,会扭曲原子力显微镜(AFM)图像,因为它们对纳米定位装置的精度产生不利影响。为了补偿磁滞对AFM横向扫描驱动器的影响,提出了一种数据驱动的前馈控制器设计算法。利用样品的前向和后向图像对提取执行器的跟踪和回向运动之间的映射。用一组未知参数定义了执行器输入输出映射对应的模型。通过定义和求解优化问题,对这些参数的取值进行优化,这些参数决定了执行器的迟滞曲线。利用遗传算法作为寻找最优值的工具。然后以基于逆的前馈控制器的形式实现迟滞映射模型,对扫描波形进行校正,得到样品的前后匹配图像。所提出的无传感器数据驱动方法易于实现,因为它不依赖于仪器,所研究的样品或成像特性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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