Intelligent tuning method of PID parameters based on iterative learning control for atomic force microscopy
Intelligent tuning method of PID parameters based on iterative learning control for atomic force microscopy
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基于迭代学习控制的原子力显微镜PID参数智能整定方法
DOI：
10.1016/j.micron.2017.09.009
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发表时间：
2018
期刊：
影响因子：
2.4
通讯作者：
Jianqiang Qian
中科院分区：
文献类型：
--
作者：
Hui Liu;Yingzi Li;Yingxu Zhang;Yifu Chen;Zihang Song;Zhenyu Wang;Suoxin Zhang;Jianqiang Qian
比例积分微分（PID）参数在原子力显微镜（AFM）成像过程中起着至关重要的作用。传统的PID参数整定方法需要大量的人力，且在无人值守的工作环境下难以整定PID参数。本文提出了一种基于迭代学习控制的PID参数智能整定方法，根据样品形貌对AFM的PID参数进行自整定。该方法通过反复行扫描直到收敛，获取PID控制器输出信号和跟踪误差的足够信息，用于计算合适的PID参数，然后再进行正常扫描学习形貌。随后，通过拟合方法获得合适的PID参数，然后将其应用于正常扫描过程。通过收敛性分析证明了该方法的可行性。仿真和实验结果表明，该方法能智能地调整AFM的PID参数，实现对不同形貌的成像，从而获得良好的跟踪性能。
Proportional-integral-derivative (PID) parameters play a vital role in the imaging process of an atomic force.microscope (AFM). Traditional parameter tuning methods require a lot of manpower and it is difficult to set PID.parameters in unattended working environments. In this manuscript, an intelligent tuning method of PID.parameters based on iterative learning control is proposed to self-adjust PID parameters of the AFM according to.the sample topography. This method gets enough information about the output signals of PID controller and.tracking error, which will be used to calculate the proper PID parameters, by repeated line scanning until.convergence before normal scanning to learn the topography. Subsequently, the appropriate PID parameters are.obtained by fitting method and then applied to the normal scanning process. The feasibility of the method is.demonstrated by the convergence analysis. Simulations and experimental results indicate that the proposed.method can intelligently tune PID parameters of the AFM for imaging different topographies and thus achieve.good tracking performance.