文献详情
Intelligent tuning method of PID parameters based on iterative learning control for atomic force microscopy
文献类型期刊
作者Liu, Hui[1];Li, Yingzi[2];Zhang, Yingxu[3];Chen, Yifu[4];Song, Zihang[5];Wang, Zhenyu[6];Zhang, Suoxin[7];Qian, Jianqiang[8]
机构
通讯作者Li, YZ; Qian, JQ (reprint author), Beihang Univ, Sch Phys & Nucl Energy Engn, Xueyuan Rd 37, Beijing 100191, Peoples R China.
来源信息年:2018  卷:104  页码范围:26-36  
期刊信息MICRON影响因子和分区  ISSN:0968-4328
关键词Intelligent tuning method; PID parameters; Iterative learning control; Atomic force microscopy
增刊正刊
摘要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.
收录情况SCIE(WOS:000418984200004)  EI(20174304294386)  PubMed(29054026)  
所属部门物理科学与核能工程学院
链接地址https://www.scopus.com/inward/record.uri?eid=2-s2.0-85031772104&doi=10.1016%2fj.micron.2017.09.009&partnerID=40&md5=fe6a6cc0d860df204aa6b2fac1cd6d33
DOI10.1016/j.micron.2017.09.009
百度学术Intelligent tuning method of PID parameters based on iterative learning control for atomic force microscopy
语言外文
人气指数334
浏览次数334
基金National Natural Science Foundation of China [61371008, 61771033]
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