Two-Stage Subspace Identification for Softsensor Development and Disturbance Estimation
Softsensors are among the key technologies in industry to realize feedback control of product quality that cannot be measured on-line. For efficient functioning of the softsensors, reduction of off-specification products, and enhancement of productivity, accurate models that can estimate product quality from measured process variables must be developed. In the present work, two-stage subspace identification (SSID) is proposed to develop highly accurate softsensors that can take into account the influence of unmeasured disturbances on estimated key variables. The two-stage SSID procedure is as follows: 1) identify a state space model by using measured input and output variables, 2) estimate unmeasured disturbance variables from residual variables, and 3) identify a state space model to estimate key variables from the estimated disturbance variables and the other measured input variables. The proposed two-stage SSID can estimate unmeasured disturbances without the assumptions that the conventional Kalman filtering technique must make. Thus it can outperform the Kalman filtering technique when innovations are not Gaussian white noises or the characteristics of disturbances do not stay constant with time.
Softsensor State estimation Modeling Subspace identification Inferential control
Seunghyun Lee Manabu Kano Shinji Hasebe
Department of Chemical Engineering,Kyoto University,Nishikyo-ku,Kyoto 615-8510,Japan
国际会议
西安
英文
2007-08-15（万方平台首次上网日期，不代表论文的发表时间）