A State Estimation Method Based on Integration of Linear and Extended Kalman Filters

Abstract

The Linear Kalman Filter (LKF) and Extended Kalman Filter (EKF) as correction-prediction observers are two common model-based filters, which are widely used to estimate the unmeasurable states and parameters in the chemical processes. The main disadvantages of LKF and EKF are low accuracy and reliability of estimation and high computational time, respectively. The main object of this work is the modification of conventional Kalman filter to cover the disadvantages of LKF and EKF. The proposed method is planned based on the systematic updating the Jacobian matrix of estimator applied on the nonlinear systems. In this regard, a linearity index is defined based on the characteristics of outputs to determine switching time between LKF and EKF to avoid unnecessary updating the Jacobian matrix. The performance of the proposed method is compared with the LKF and EKF considering four chemical benchmarks as the nonlinear state space models. Altought EKF and proposed filter present similar precision to estimate unmeasurable states, the proposed method has a lower computational time.