A robust iterative learning observer based fault diagnosis of time delay nonlinear systems

Document Type

Conference Proceeding

Publication Date

1-1-2002

Publication Title

IFAC Proceedings Volumes (IFAC-PapersOnline)

Volume

15

Issue

1

First Page

401

Last Page

406

Abstract

An Iterative Learning Observer (ILO) updated successively and iteratively by immediate past system output error and ILO input is proposed for a class of time-delay nonlinear systems for the purpose of robust fault diagnosis. The proposed observer can estimate the system state as well as disturbances and actuator faults so that ILO can still track the post-fault system. In addition, the observer can attenuate slow varying output measurement disturbances. The ILO fault detection approach is then applied to automotive engine fault detection and estimation. Simulations show that the proposed ILO fault detection and estimation strategy is successful.

DOI

10.3182/20020721-6-es-1901.00798

ISSN

14746670

ISBN

9783902661746

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