Robust fault detection using iterative learning observer for nonlinear systems

Liling Ma; Junzheng Wang; Shoukun Wang

Robust fault detection using iterative learning observer for nonlinear systems

Liling Ma^*, Junzheng Wang, Shoukun Wang

^*Corresponding author for this work

School of Automation

Research output: Contribution to conference › Paper › peer-review

10 Citations (Scopus)

Abstract

A robust fault detection scheme for a class of nonlinear systems with modeling uncertainty and inaccessible states was presented. Only the inputs and outputs of the system can be measured. A nonlinear iterative learning observer was utilized to produce the residual that was robust to uncertainty. The stability of the fault detection scheme under certain assumptions was analyzed. An example demonstrates the efficiency of the proposed fault detection strategy.

Original language	English
Pages	1724-1726
Number of pages	3
Publication status	Published - 2004
Event	WCICA 2004 - Fifth World Congress on Intelligent Control and Automation, Conference Proceedings - Hangzhou, China Duration: 15 Jun 2004 → 19 Jun 2004

Conference

Conference	WCICA 2004 - Fifth World Congress on Intelligent Control and Automation, Conference Proceedings
Country/Territory	China
City	Hangzhou
Period	15/06/04 → 19/06/04

Keywords

Fault detection
Iterative learning
Nonlinear system
Observer
Robustness

Cite this

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title = "Robust fault detection using iterative learning observer for nonlinear systems",

abstract = "A robust fault detection scheme for a class of nonlinear systems with modeling uncertainty and inaccessible states was presented. Only the inputs and outputs of the system can be measured. A nonlinear iterative learning observer was utilized to produce the residual that was robust to uncertainty. The stability of the fault detection scheme under certain assumptions was analyzed. An example demonstrates the efficiency of the proposed fault detection strategy.",

keywords = "Fault detection, Iterative learning, Nonlinear system, Observer, Robustness",

author = "Liling Ma and Junzheng Wang and Shoukun Wang",

year = "2004",

language = "English",

pages = "1724--1726",

note = "WCICA 2004 - Fifth World Congress on Intelligent Control and Automation, Conference Proceedings ; Conference date: 15-06-2004 Through 19-06-2004",

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TY - CONF

T1 - Robust fault detection using iterative learning observer for nonlinear systems

AU - Ma, Liling

AU - Wang, Junzheng

AU - Wang, Shoukun

PY - 2004

Y1 - 2004

N2 - A robust fault detection scheme for a class of nonlinear systems with modeling uncertainty and inaccessible states was presented. Only the inputs and outputs of the system can be measured. A nonlinear iterative learning observer was utilized to produce the residual that was robust to uncertainty. The stability of the fault detection scheme under certain assumptions was analyzed. An example demonstrates the efficiency of the proposed fault detection strategy.

AB - A robust fault detection scheme for a class of nonlinear systems with modeling uncertainty and inaccessible states was presented. Only the inputs and outputs of the system can be measured. A nonlinear iterative learning observer was utilized to produce the residual that was robust to uncertainty. The stability of the fault detection scheme under certain assumptions was analyzed. An example demonstrates the efficiency of the proposed fault detection strategy.

KW - Fault detection

KW - Iterative learning

KW - Nonlinear system

KW - Observer

KW - Robustness

UR - http://www.scopus.com/inward/record.url?scp=4444359962&partnerID=8YFLogxK

M3 - Paper

AN - SCOPUS:4444359962

SP - 1724

EP - 1726

T2 - WCICA 2004 - Fifth World Congress on Intelligent Control and Automation, Conference Proceedings

Y2 - 15 June 2004 through 19 June 2004

ER -

Robust fault detection using iterative learning observer for nonlinear systems

Abstract

Conference

Keywords

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