Korayem Moharram H, Esmaeilzadehha Soliman
Robotic Research Laboratory, Center of Excellence in Experimental Solid Mechanics and Dynamics, School of Mechanical Engineering, Iran University of Science and Technology, Tehran, Iran.
Faculty of Electrical, Biomedical and Mechatronics Engineering, Qazvin Branch, Islamic Azad University, Qazvin, Iran.
Microsc Res Tech. 2019 Jul;82(7):993-1003. doi: 10.1002/jemt.23246. Epub 2019 Mar 6.
Development of nanotechnology has given rise to various applications, including the nano-manipulation process within small-size environments. The implementation of such processes requires the use of tools and proper equipment and understanding of various factors influencing it. One such tool is the atomic force microscope (AFM) and its probe, used for imaging surfaces and manipulation tools. The AFM probe is the most important element of the AFM with a key role in system function. The dynamic analysis and control of AFM are necessary to increase efficiency. In this paper, a model of AFM is reviewed and rewritten by considering various cantilever probes, including rectangular, V-shaped, and dagger. The AFM actuator was modeled and analyzed on uncertain conditions. The position of the stage was controlled to the desired position through the desired motion profiles. To overcome the problem of model nonlinearity, a neural network (NN) sliding mode controller was used to optimize the controller parameter and provide the desired output. The simulation of system was performed by the effective parameters, its control was implemented, and the results were analyzed. The simulation revealed that the modified sliding mode controller with learnable NN improved controller performance by decreasing the rise time and eliminating the overshot.
纳米技术的发展催生了各种应用，包括在小尺寸环境中的纳米操纵过程。实施此类过程需要使用工具和适当的设备，并了解影响它的各种因素。原子力显微镜（AFM）及其探针就是这样一种工具，用于表面成像和操纵工具。AFM探针是AFM中最重要的元素，在系统功能中起着关键作用。对AFM进行动态分析和控制对于提高效率是必要的。本文通过考虑各种悬臂探针，包括矩形、V形和匕首形，对AFM模型进行了回顾和改写。对AFM执行器在不确定条件下进行了建模和分析。通过期望的运动轮廓将工作台的位置控制到期望位置。为克服模型非线性问题，采用神经网络（NN）滑模控制器来优化控制器参数并提供期望输出。通过有效参数对系统进行了仿真，实现了其控制，并对结果进行了分析。仿真结果表明，具有可学习神经网络的改进滑模控制器通过减少上升时间和消除过冲提高了控制器性能。
