Process Monitoring and Parameter Estimation via Unscented Kalman Filtering

Nonlinear estimation techniques play an important role for process monitoring since some states and most of the parameters cannot be directly measured. This paper investigates the use of several estimation algorithms such as linearized Kalman filter (LKF), extended Kalman filter (EKF), unscented Kalman filter (UKF) and moving horizon estimation (MHE) for nonlinear systems with special emphasis on UKF as it is a relatively new technique. Detailed case studies show that UKF has advantages over EKF for highly nonlinear unconstrained estimation problems while MHE performs better for systems with constraints.