Open AccessImproved estimation and fault detection scheme for a class of non‐linear hybrid systems using time delayed adaptive CD state estimator
Author(s) -
Chatterjee Sayanti,
Sadhu Smita,
Ghoshal Tapan Kumar
Publication year - 2017
Publication title -
iet signal processing
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.384
H-Index - 42
eISSN - 1751-9683
pISSN - 1751-9675
DOI - 10.1049/iet-spr.2016.0380
Subject(s) - estimator , kalman filter , control theory (sociology) , benchmark (surveying) , fault detection and isolation , computer science , algorithm , linear system , mathematics , statistics , artificial intelligence , mathematical analysis , actuator , control (management) , geodesy , geography
An improved fault detection scheme for a non‐linear hybrid system with delayed measurement by using a modified non‐linear adaptive state estimator is proposed. The proposed estimator performs acceptably even when the (i) covariance of the measurement noise is unknown and also (ii) when the measurements are delayed. The algorithm for the proposed estimator called time‐delayed R ‐adaptive central difference Kalman filter (TD‐RACDKF) uses a modified R‐adaptive 2nd order Central Difference estimator, also called Central Difference Kalman Filter (CDKF) in some literature. Algorithm for the proposed TD‐RACDKF has been presented and its performance evaluated as well as characterised with Monte Carlo simulation on two standard non‐linear systems with delayed measurements. The characterisation includes comparison with existing estimators. Having demonstrated the improved performance of the proposed state estimator, its use for fault detection of non‐linear hybrid systems was investigated. The performance of the fault detection scheme has been illustrated with the help of a benchmark non‐linear hybrid system, namely ‘three tank system’ and by comparison with a previously available extended KF‐based estimator.