Open AccessKalman Randomized Joint UKF Algorithm for Dual Estimation of States and Parameters in a Nonlinear System
Author(s) -
Behrouz Safarinejadian,
Navid Vafamand
Publication year - 2015
Publication title -
journal of electrical engineering and technology/journal of electrical engineering and technology
Language(s) - English
Resource type - Journals
eISSN - 2093-7423
pISSN - 1975-0102
DOI - 10.5370/jeet.2015.10.3.1212
Subject(s) - extended kalman filter , kalman filter , control theory (sociology) , unscented transform , nonlinear system , joint (building) , invariant extended kalman filter , fast kalman filter , algorithm , computer science , engineering , artificial intelligence , physics , architectural engineering , control (management) , quantum mechanics
This article presents a new nonlinear joint (state and parameter) estimation algorithm based on fusion of Kalman filter and randomized unscented Kalman filter (UKF), called Kalman randomized joint UKF (KR-JUKF). It is assumed that the measurement equation is linear. The KRJUKF is suitable for time varying and severe nonlinear dynamics and does not have any systematic error. Finally, joint-EKF, dual-EKF, joint-UKF and KR-JUKF are applied to a CSTR with cooling jacket, in which production of propylene glycol happens and performance of KR-JUKF is evaluated.