Incorporating delayed measurements in an improved high-degree cubature Kalman filter for the nonlinear state estimation of chemical processes

Abstract

The on-line estimation of process quality variables has a large impact on the advanced monitoring and control techniques of chemical processes. The present study offers an improved high-degree cubature Kalman filter (HCKF) to solve the nonlinear state estimation problem of high-dimensional chemical processes. We substituted the Cholesky decomposition in the HCKF filter with a diagonalization transformation of the matrix. In addition, we enhanced numerical stability and estimation accuracy. On this basis, we present one nonlinear state estimation method based on the sample-state augmentation and improved HCKF to handle issues with delayed measurements. Finally, we used the nonlinear state estimation experiments for the polymerization process to validate the proposed method. The numerical results indicated the achievement of state estimation with higher accuracy and better stability following the effective utilization of the delayed measurements for nonlinear chemical processes.