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Reduced-Order Distributed Unknown Input Observer for State Estimation and Unknown Input Reconstruction.

Shuqing Tang1, Zongjun Zhu2, Xufeng Ling3

  • 1Shanghai Fiswft Intelligent Technology Co., Ltd., Shanghai 201800, China.

Sensors (Basel, Switzerland)
|February 27, 2026
PubMed
Summary
This summary is machine-generated.

This study introduces a novel reduced-order distributed unknown input observer (DUIO) for estimating states, unknown inputs (UI), and measurement noise (MN) in uncertain systems. The DUIO ensures asymptotic convergence for improved system analysis and control.

Keywords:
distributed reduced-order observerdistributed unknown input observerinterval observermeasurement noise reconstructionunknown input reconstruction

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Area of Science:

  • Control Systems Engineering
  • Distributed Observer Design
  • Signal Processing

Background:

  • Traditional observer designs address state estimation, unknown input reconstruction (UIR), and measurement noise reconstruction (MNR) individually.
  • Simultaneous UIR and MNR in distributed observer systems remain underexplored, presenting a significant research gap.
  • Existing methods lack comprehensive solutions for distributed systems with both unknown inputs and measurement noise.

Purpose of the Study:

  • To design a reduced-order distributed unknown input observer (DUIO) for uncertain systems.
  • To achieve asymptotic convergence for simultaneous estimation of states, unknown inputs (UI), and measurement noise (MN).
  • To address the limitations of current distributed observer designs in handling complex system uncertainties.

Main Methods:

  • Introduction of an auxiliary output to transform local systems, eliminating measurement noise from the output.
  • Development of a distributed reduced-order observer for the transformed local system to yield state estimations.
  • Design of an unknown input reconstruction (UIR) method based on interval observers and state estimation, ensuring asymptotic UI estimation and decoupling control input.

Main Results:

  • The proposed DUIO provides asymptotically convergent estimations for system states, unknown inputs, and measurement noise.
  • The UIR component effectively estimates the real unknown input and decouples the control input.
  • Simulation results validate the effectiveness and advantages of the developed distributed observer method.

Conclusions:

  • The designed reduced-order DUIO successfully addresses the simultaneous estimation of states, UI, and MN in distributed systems.
  • The method offers a robust solution for uncertain systems, enhancing observer capabilities in complex networked environments.
  • This work contributes a novel approach to distributed observer design, paving the way for more advanced control strategies.