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A Luenberger-like State Estimation with False Data Injection on Input and Model Mismatch: LMI Approach

EasyChair Preprint 8398

17 pagesDate: July 5, 2022

Abstract

This paper investigates a proportional-integral Luenberger-like state estimation for continous linear systems under disturbed inputs or false data inhection attacks on inputs and model mismatch. A general design algorithm is proposed to estimate the states of a linear system in the presence of model mismatch, external disturbance, and disturbed inputs / false data injection. In reality, the absence of the conditions is very rare, therefore it is required to develop a method for designing robust observers in presence of uncertainties, external disturbances, and disturbed inputs.  Lyapunov method and LMI theory have been used to obtain the gains of the Proportional-Integral Observer (PIO). The stability of the proposed PI observer is proved and with a numerical example, its efficiencies have been shown under different cases including model mismatch, disturbances, and attacks. The results illustrate that the proposed algorithm can estimate the state of a system according to defined conditions.  This paper investigates a proportional-integral Luenberger-like state estimation for continous linear systems under disturbed inputs or false data inhection attacks on inputs and model mismatch. A general design algorithm is proposed to estimate the states of a linear system in the presence of model mismatch, external disturbance, and disturbed inputs / false data injection. In reality, the absence of the conditions is very rare, therefore it is required to develop a method for designing robust observers in presence of uncertainties, external disturbances, and disturbed inputs.  Lyapunov method and LMI theory have been used to obtain the gains of the Proportional-Integral Observer (PIO). The stability of the proposed PI observer is proved and with a numerical example, its efficiencies have been shown under different cases including model mismatch, disturbances, and attacks.

Keyphrases: False Data Injection., Luenberger-like observer, model mismatch

BibTeX entry
BibTeX does not have the right entry for preprints. This is a hack for producing the correct reference:
@booklet{EasyChair:8398,
  author    = {Mohammad Tahmasbi and Alireza Farbod and Ahmadreza Fakhari},
  title     = {A Luenberger-like State Estimation with False Data Injection on Input and Model Mismatch: LMI Approach},
  howpublished = {EasyChair Preprint 8398},
  year      = {EasyChair, 2022}}
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