State Estimation and Fault Detection of Fractional Order Nonlinear Systems

This study is devoted to robust state estimation and fault detection for nonlinear discrete fractional order systems using a novel fractional order filter algorithm. While noise and disturbance effects can suppress or even disturb the state estimation, the proposed filter can preciously estimate the...

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Bibliographic Details
Main Authors: Tabatabaei, Mahmood, Zarei, Jafar, Razavi-Far, Roozbeh, Saif, Mehrdad
Format: Conference Proceeding
Language:English
Subjects:
Covariance matrices
Disturbance decoupling
Extended unknown input observer
Fault detection
Fractional Kalman filter
Fractional order systems
Kalman filters
Mathematical model
Noise measurement
Observers
State estimation
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Description
Summary:This study is devoted to robust state estimation and fault detection for nonlinear discrete fractional order systems using a novel fractional order filter algorithm. While noise and disturbance effects can suppress or even disturb the state estimation, the proposed filter can preciously estimate the states of nonlinear fractional order systems. Disturbance decoupling approach is the fundamental basis for the proposed filter to make it robust against unknown inputs. Simulation results illustrate the advantages of the proposed filter for state estimation and fault detection of nonlinear fractional order systems in the presence of both noise and disturbance.
ISSN:1558-3899
DOI:10.1109/MWSCAS.2018.8623870