Parameter estimation method for coupled tank system using Dual Extended Kalman filter

Seung, Ji-Hoon; Lee, Deok Jin; Chong, Kil-To

doi:10.1109/iccas.2013.6704176

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Cited by 3 publications

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“…In several literatures, results demonstrate that the dual estimators can more effectively compute the system states in the case of unknown system parameters, compared to conventional ones [25][26][27][28][29]. Also, it is proved that the original UKF has a systematic error [13,30].…”

Section: Second Form Of Sirmentioning

confidence: 99%

Kalman Randomized Joint UKF Algorithm for Dual Estimation of States and Parameters in a Nonlinear System

Safarinejadian¹,

Vafamand²

2015

Journal of Electrical Engineering and Technology

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-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 KR-JUKF 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.

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