Conference article

An FMI-based Framework for State and Parameter Estimation

Marco Bonvini
Simulation Research Group, Lawrence Berkeley National Laboratory, Berkeley, CA, USA

Michael Wetter
Simulation Research Group, Lawrence Berkeley National Laboratory, Berkeley, CA, USA

Michael D. Sohn
Simulation Research Group, Lawrence Berkeley National Laboratory, Berkeley, CA, USA

Download articlehttp://dx.doi.org/10.3384/ecp14096647

Published in: Proceedings of the 10th International Modelica Conference; March 10-12; 2014; Lund; Sweden

Linköping Electronic Conference Proceedings 96:68, p. 647-656

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Published: 2014-03-10

ISBN: 978-91-7519-380-9

ISSN: 1650-3686 (print), 1650-3740 (online)

Abstract

This paper proposes a solution for creating a model-based state and parameter estimator for dynamic systems described using the FMI standard. This work uses a nonlinear state estimation technique called unscented Kalman filter (UKF); together with a smoother that improves the reliability of the estimation. The algorithm can be used to support advanced control techniques (e.g.; adaptive control) or for fault detection and diagnostics (FDD). This work extends the capabilities of any modeling framework compliant with the FMI standard version 1.0.

Keywords

Nonlinear State and Parameter Estimation; Unscented Kalman Filter (UKF); Smoothing; Functional Mockup Interface (FMI); Fault Detection and Diagnosis (FDD)

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