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

作　　者：董晓坤[1,2] 方勇纯[1,2] 张雪波 

机构地区：[1]南开大学机器人与信息自动化研究所,天津300071 [2]天津市智能机器人技术重点实验室,天津300071

出　　处：《控制理论与应用》2015年第8期1058-1063,共6页Control Theory & Applications

基　　金：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~~

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.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.

关 键 词：原子力显微镜 成像方法 广义预测控制 自整定 

分 类 号：TH742[机械工程—光学工程]