This paper presents a multivariable generalized predictive controller with proportion and integration structure by modifying the quadratic criterion of the usual MGPC. The control performance has been improved greatl...This paper presents a multivariable generalized predictive controller with proportion and integration structure by modifying the quadratic criterion of the usual MGPC. The control performance has been improved greatly. The effectiveness of the controller is demonstrated by the simulation result.展开更多
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...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.展开更多
文摘This paper presents a multivariable generalized predictive controller with proportion and integration structure by modifying the quadratic criterion of the usual MGPC. The control performance has been improved greatly. The effectiveness of the controller is demonstrated by the simulation result.
文摘在原子力显微镜(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.
