An Ensemble Kalman Filter for Systems Governed by Differential Algebraic Equations (DAEs)

• Published in: IFAC Proceedings Volumes Vol. 45; no. 15; pp. 531 - 536
• Main Authors: Puranik, Yash, Bavdekar, Vinay A., Patwardhan, Sachin C., Shah, Sirish L.
• Format: Journal Article
• Language: English
• Published: 2012
• Subjects: differential algebraic systems; ensemble Kalman filter; nonlinear state estimation; nonlinear state estimation; differential algebraic systems; ensemble Kalman filter
• ISSN: 1474-6670
• DOI: 10.3182/20120710-4-SG-2026.00167

Many process systems can be realistically described by a set of nonlinear differential algebraic equations (DAEs). To carry out state estimation of these systems, the conventional sequential Bayesian estimation schemes have to be modified to accommodate nonlinear algebraic constraints. In this work, we present a modified formulation of the Ensemble Kalman Filter for state estimation of systems described by DAEs. The proposed formulation can utilize measurements obtained either from, the differential or algebraic states. The efficacy of the proposed EnKF formulation is demonstrated by simulating two benchmark examples from the literature. The simulation results indicate that the proposed EnKF algorithm can efficiently track both the differential and the algebraic states with reasonable accuracy.