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

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

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.