袁帅,尧晓,栾方军,郭耸,冯吉远.基于随机方法的AFM探针位置最优估算研究[J].仪器仪表学报,2017,38(9):2120-2129
基于随机方法的AFM探针位置最优估算研究
Optimal position estimation of AFM tip based on stochastic approach
  
DOI:
中文关键词:  探针精确定位  运动模型  局部扫描  Kalman滤波  参数标定
英文关键词:tip precise positioning  motion model  local scan  Kalman filtering  parameter calibration
基金项目:中国国家自然科学青年基金(61305125)、沈阳建筑大学学科内容教育工程(XKHY2-66)、学校自然科学基金(2014068, 2017028)、国家博士后基金(2013M530955, 2014T70265)项目资助
作者单位
袁帅 1.沈阳建筑大学信息与控制工程学院沈阳110168; 2.中国科学院沈阳自动化研究所沈阳110016 
尧晓 沈阳建筑大学信息与控制工程学院沈阳110168 
栾方军 沈阳建筑大学信息与控制工程学院沈阳110168 
郭耸 沈阳建筑大学信息与控制工程学院沈阳110168 
冯吉远 沈阳建筑大学信息与控制工程学院沈阳110168 
AuthorInstitution
Yuan Shuai 1. School of information and control engineering, Shenyang Jianzhu University, Shenyang 110168, China; 2. Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, China 
Yao Xiao School of information and control engineering, Shenyang Jianzhu University, Shenyang 110168, China 
Luan Fangjun School of information and control engineering, Shenyang Jianzhu University, Shenyang 110168, China 
Guo Song School of information and control engineering, Shenyang Jianzhu University, Shenyang 110168, China 
Feng Jiyuan School of information and control engineering, Shenyang Jianzhu University, Shenyang 110168, China 
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中文摘要:
      由于受到驱动器PZT (PbZrTiO3) 非线性、系统温漂与其他不确定因素的影响,原子力显微镜(AFM) 探针在任务空间的位置存在不确定性。这严重影响了AFM探针观测与操作的效率,如何减小探针位置的不确定性, 实现AFM探针的精确定位成为亟待解决的问题。针对此问题, 提出用概率分布的方式描述探针位置的不确定性,通过建立探针运动模型, 结合基于局部扫描的观测模型, 采用Kalman滤波对探针位置进行最优估算。针对算法的实现, 设计了模型参数标定方案。通过仿真和实验的结果验证了算法的有效性与可行性,实现了探针在任务空间中的精确定位,提高了纳米操作效率。
英文摘要:
      Due to nonlinearity, system temperature drift and other uncertainties of PZT (PbZrTiO3), there exists uncertainty for the position of AFM (Atomic Force Microscopy) tip in the task space. It seriously affects the observation and operation efficiency of AFM tip. Thus, to reduce the uncertainty of tip position, and achieve precise positioning becomes an urgent issue to be solved. Firstly, this work represents the uncertainties of tip position in the task space by using the probability distribution. Then, tip motion model is established, and local scan based observation model is combined to estimate optimal tip position by using the Kalman filter. In addition, a model parameter calibration scheme is designed to implement the proposed method. The validity and feasibility of the algorithm are verified by the simulation and experimental results. AFM tip precise positioning can be realized in the task space, and the efficiency of nanomanipulation can be improved.
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