Observability and State Estimation for Smooth and Nonsmooth Differential Algebraic Equation Systems

Fuente: arXiv
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Main Authors: Abdelfattah, Hesham, Eisa, Sameh A., Stechlinski, Peter
Format: Preprint
Published: 2025
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author Abdelfattah, Hesham
Eisa, Sameh A.
Stechlinski, Peter
author_facet Abdelfattah, Hesham
Eisa, Sameh A.
Stechlinski, Peter
contents In this work, we extend the sensitivity-based rank condition (SERC) test for local observability to another class of systems, namely smooth and nonsmooth differential-algebraic equation (DAE) systems of index-1. The newly introduced test for DAEs, which we call the lexicographic SERC (L-SERC) observability test, utilizes the theory of lexicographic differentiation to compute sensitivity information. Moreover, the newly introduced L-SERC observability test is useful in the context of partial observability as it can judge which states are observable and which are not. Additionally, we introduce a novel sensitivity-based extended Kalman filter (S-EKF) algorithm for state estimation, applicable to both smooth and nonsmooth DAE systems. Finally, we apply the newly developed S-EKF to estimate the states of a wind turbine power system model.
format Preprint
id arxiv_https___arxiv_org_abs_2508_18476
institution arXiv
publishDate 2025
record_format arxiv
spellingShingle Observability and State Estimation for Smooth and Nonsmooth Differential Algebraic Equation Systems
Abdelfattah, Hesham
Eisa, Sameh A.
Stechlinski, Peter
Dynamical Systems
In this work, we extend the sensitivity-based rank condition (SERC) test for local observability to another class of systems, namely smooth and nonsmooth differential-algebraic equation (DAE) systems of index-1. The newly introduced test for DAEs, which we call the lexicographic SERC (L-SERC) observability test, utilizes the theory of lexicographic differentiation to compute sensitivity information. Moreover, the newly introduced L-SERC observability test is useful in the context of partial observability as it can judge which states are observable and which are not. Additionally, we introduce a novel sensitivity-based extended Kalman filter (S-EKF) algorithm for state estimation, applicable to both smooth and nonsmooth DAE systems. Finally, we apply the newly developed S-EKF to estimate the states of a wind turbine power system model.
title Observability and State Estimation for Smooth and Nonsmooth Differential Algebraic Equation Systems
topic Dynamical Systems
url https://arxiv.org/abs/2508.18476