引用本文:董晓坤,方勇纯,张雪波.原子力显微镜系统广义预测控制与成像[J].控制理论与应用,2015,32(8):1058~1063.[点击复制]
DONG Xiao-kun,FANG Yong-chun,Zhang Xue-bo.Generalized predictive control and imaging for atomic force microscope systems[J].Control Theory and Technology,2015,32(8):1058~1063.[点击复制]
原子力显微镜系统广义预测控制与成像
Generalized predictive control and imaging for atomic force microscope systems
摘要点击 2576  全文点击 1650  投稿时间：2014-07-18  修订日期：2015-09-13
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DOI编号  10.7641/CTA.2015.40674
  2015,32(8):1058-1063
中文关键词  原子力显微镜  成像方法  广义预测控制  自整定
英文关键词  atomic force microscopy  imaging method  generalized predictive control  self-tuning
基金项目  国家自然科学基金项目(61127006, 61203333), 天津市自然科学基金项目(13JCQNJC03200)资助.
作者单位E-mail
董晓坤 南开大学 机器人与信息自动化研究所
天津市智能机器人技术重点实验室 		dongxk@robot.nankai.edu.cn 
方勇纯* 南开大学 机器人与信息自动化研究所
天津市智能机器人技术重点实验室 		yfang@nankai.edu.cn 
张雪波 南开大学 机器人与信息自动化研究所
天津市智能机器人技术重点实验室 		 
中文摘要
      在原子力显微镜(atomic force microscope, AFM)扫描样品时, 控制参数调节困难, 依赖于操作经验. 本文基于在线动态模型辨识, 提出了一种AFM系统广义预测自校正控制与成像方法. 首先, 利用CARIMA(controlled autoregressive and moving-average)参数模型来描述局部线性化后的AFM系统模型, 并通过在线动态模型辨识得到线性化模型的参数; 基于该模型, 采用基于GPC(generalized predictive control)的优化方法, 在线求解类PI(proportional integral)控制器的参数, 进而得到一种具有控制参数自动调整功能的AFM成像方法. 为了验证本文方法的有效性, 进行了仿真与实验测试. 结果表明, 在AFM扫描速度不同或PI参数选择不恰当的情况下, 该方法能够自动地调整控制器参数, 从而减小控制误差, 提高成像精度.
英文摘要
      When an atomic force microscope (AFM) is employed to scan a sample, the proper adjustment of control parameters is usually difficult; it needs operator’s experience. To address this problem, we present a generalized predictive control and imaging scheme based on the online identification of dynamic system model, which achieves self-tuning of control gains. Specifically, the controlled autoregressive and moving-average model (CARIMA) is adopted to describe the linearized AFM system, whose parameters are obtained through online dynamic model identification. Then, the generalized predictive control (GPC) optimization method is applied to calculate the parameters of a quasi-proportional integral (PI) controller which automatically controls the AFM system gains. Simulation and experimental results demonstrate that the proposed method can adjust the control gains automatically to reduce the control error and improve the imaging precision, even when the scanning speed is changed or the control parameters are chosen improperly.