An FMI-based Framework for State and Parameter Estimation

Abstract

This paper proposes a solution for creating a model-based state and parameter estimator for dynamic sys-tems described using the FMI standard. This work uses a nonlinear state estimation technique called un-scented Kalman filter (UKF), together with a smoother that improves the reliability of the estimation. The al-gorithm can be used to support advanced control tech-niques (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.