一种融合无时滞测量值和含时滞测量值的状态估计方法  

State estimation approach by incorporating measurements with delay-free and time delay

作　　者：王金萍 赵忠盖 刘飞 

机构地区：[1]江南大学轻工过程先进控制教育部重点实验室,江苏无锡214122

出　　处：《化工学报》2016年第3期940-946,共7页CIESC Journal

基　　金：国家自然科学基金项目(61573169);国家自然科学重点基金项目(61134007)~~

摘　　要：在很多工业过程中,常常可获得两种测量数据,无时滞测量值和含时滞测量值,其中,无时滞测量值直接由传感器在线测得,即时却精度较低,含时滞测量值通过人工实验分析离线得到,精度高却有时滞。引入状态增广卡尔曼滤波法对上述两种数据进行融合以估计当前状态值。考虑到无时滞测量值建立的在线软测量模型存在不可避免的模型不匹配问题,引入模型偏差作为待估计状态,通过离线测量值对其进行估计,从而实现对在线软测量模型的校正。最后将所提方法运用到线性化的非线性二元蒸馏塔模型中估计填料压板各成分浓度,取得了良好效果。In many industrial processes, in addition to the online measurements with delay-free and low inaccuracy, there exist delayed measurements accurately obtained by laboratory analysis. The augmented state Kalman filter is employed to estimate the state by incorporating both the delayed and the delay-free measurements. To overcome the model-plant mismatch of the online soft-sensor model built by the delay-free measurements, the model deviation is employed to update the soft-sensor model. To follow the model drift, the model deviation is treated as a state, and it will be estimated when the offline measurements arrive. In the end the proposed method is used to estimate the tray compositions in the linearized nonlinear binary distillation column model and obtains good results.

关 键 词：时滞 状态增广卡尔曼滤波 测量模型 校正 蒸馏塔 

分 类 号：TN713.3[电子电信—电路与系统] TP274.2[自动化与计算机技术—检测技术与自动化装置]