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Fault estimation and prediction for nonlinear stochastic system with intermittent observations
Author(s) -
Ding Bo,
Fang Huajing
Publication year - 2017
Publication title -
international journal of robust and nonlinear control
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.361
H-Index - 106
eISSN - 1099-1239
pISSN - 1049-8923
DOI - 10.1002/rnc.3925
Subject(s) - kalman filter , fault (geology) , nonlinear system , control theory (sociology) , extended kalman filter , moving horizon estimation , computer science , estimation , fault detection and isolation , filter (signal processing) , engineering , artificial intelligence , control (management) , seismology , computer vision , geology , physics , systems engineering , quantum mechanics
Summary This paper is concerned with the fault estimation and prediction problems for a class of nonlinear stochastic systems with intermittent observations. Based on the extended Kalman filter and Kalman filter, the fault and state are simultaneously estimated, and then, it is extended to the case of intermittent observations. Meanwhile, the boundedness of the estimation error is also discussed. Once the fault is detected, the parameters of each fault are identified by the linear regression method. Then, the future fault signal can be predicted by the parameters of the fault. The effectiveness of the proposed algorithm is verified by the simulation of the 3‐tank system.