Simultaneous state-estimator tuning and parameter estimation for systems with nonstationary disturbances, multi-rate data, and measurement delays

Abstract

Model-based monitoring and control of chemical and biochemical processes rely on state estimators such as extended Kalman filters (EKFs) to ensure accurate online model predictions. Accurate predictions depend on appropriate model parameters and suitable state-estimator tuning factors. Extensions to our previously developed simultaneous parameter estimation and tuning (SPET) method are proposed so that SPET can be used for systems with nonstationary disturbances, time-varying parameters, multi-rate data, and measurement delays. A continuous stirred tank reactor (CSTR) case study with simulated data is used to illustrate and test the proposed method. Superior online model predictions and state-estimator performance are achieved using SPET compared to a traditional approach for parameter estimation and EKF tuning, with improvements in the average sum-of-squared prediction errors ranging from 3% to 52% for the scenarios tested. The SPET approach will also be useful for more-advanced state estimators that require the same tuning information as EKFs.