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Cited by 835

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Design of unknown input observers and robust fault detection filters

CHEN, JIE, PATTON, RON J., ZHANG, HONG-YUE·1996·International Journal of Control

Abstract

Fault detection filters are a special class of observers that can generate directional residuals for the purpose of fault isolation. This paper proposes a new approach to design robust (in the disturbance de-coupling sense) fault detection filters which ensure that the residual vector, generated by this filter, has both robust and directional properties. This is done by combining the unknown input observer and fault detection filter principles. The paper proposes a new full-order unknown input observer, and gives necessary and sufficient conditions for its existence. After the disturbance de-coupling conditions are satisfied, the remaining design freedom can be used to make the residual have the directional property, based on the fault detection filter principle. A nonlinear jet engine system is used to illustrate the robust fault isolation approach presented. It is shown that linearization errors can be approximately treated as unknown disturbances and be de-coupled in the design of a robust fault detection filter. Simulation results show that mis-isolation of faults can be avoided using the robust scheme.

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Metadata

Authors
CHEN, J., PATTON, R. J., & ZHANG, H.-Y.
Venue
International Journal of Control
Published
1996-01
Volume
63
Issue
1
Pages
85-105
ISSN
  • 0020-7179 (ISSN-L)
  • 1366-5820
OpenAlex
W2083514989
Cited by
835
References
20
FWCI
34.85
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: Design of unknown input observers and robust fault detection filters. https://app.cochat.ai/papers/10.1080/00207179608921833
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CHEN, J., PATTON, R. J., & ZHANG, H.-Y. (1996). Design of unknown input observers and robust fault detection filters. International Journal of Control. https://doi.org/10.1080/00207179608921833

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.