Work

Data-Driven Observers Design for Descriptor Systems

Year: 2026

Type: preprint

Abstract: State estimation constitutes a core task in monitoring, supervision, and control of dynamic systems. This paper proposes a data-driven framework for the design of state observers for descriptor systems. Necessary and sufficient conditions for the existence of a standard state observer are derived purely from data under mild assumptions. When the system is subject to unknown inputs, we further extend the framework to the data-driven design method for full-order unknown input observer (UIO). Notably, for both the standard state observer and the UIO, we establish the mathematical equivalence between the proposed data-driven existence conditions and classical model-based ones. Moreover, the data-driven approach is applied to the design of extended state observers, enabling simultaneous estimation of system states and disturbances via system augmentation. Numerical simulations validate the effectiveness of the proposed methods.

Source: arXiv (Cornell University)

DOI: 10.48550/arxiv.2604.11345

Authors: Zhang, Yuan, Wang, Yu, Huang, Keke, Sun, Zhongqi, Fernando, Tyrone

Institutions: Beijing Institute of Technology, Central South University, The University of Western Australia

Topic: Control Systems and Identification

Subfield: Control and Systems Engineering

Field: Engineering

Domain: Physical Sciences

Open Access status: green

Funders: National Natural Science Foundation of China, National Key Research and Development Program of China

Award: G4705530201

OpenAlex ID: W7154229288

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