[KSCI] Korea Science Citation Index Service

http://dx.doi.org/10.5391/IJFIS.2006.6.1.001

A Fault Detection System Design for Uncertain Fuzzy Systems

Yoo, Seog-Hwan (School of Electronic Engineering, Daegu University)
Choi, Byung-Jae (School of Electronic Engineering, Daegu University)

Publication Information

International Journal of Fuzzy Logic and Intelligent Systems / v.6, no.1, 2006 , pp. 1-5 More about this Journal

Abstract

This paper deals with a fault detection system design for uncertain nonlinear systems modelled as T-S fuzzy systems with the integral quadratic constraints. In order to generate a residual signal, we used a left coprime factorization of the T-S fuzzy system. From the filtered signal of the residual generator, the fault occurence can be detected effectively. A simulation study with nuclear steam generator level control system shows that the suggested method can be applied to detect the fault in actual applications.

Keywords

T-S fuzzy systems; Left coprime factorization; Steam generator; Linear matrix inequality; Fault detection;

Citations & Related Records

Reference

1	R. Patton and J. Chen, Robust fault detection using eigenstructure assignment : A tutorial consideration and some new results, In Proc. of the 30th CDC, England, pp.2242-2247, 1991
2	E. Irving, C. Miossec and J. Tassart, Toward efficient full automatic operation of the pwr steam generator with water level adaptive control. In 2nd Int. Conf. Boiler Dynamics and Control in Nuclear Power Stations, Bournemouth, U.K., Oct. pp.309-329, 1979
3	M.G. Na and H.C. No, Quantitative evaluation of swelling or shrinking level contributions in steam generators using spectrum analysis, Ann. Nucl. Energy, vol.20, no.10, pp.659-666, Oct. 1993 DOI ScienceOn
4	E. Collins and T. Song, Multiplier based robust H $_{\infty}$ estimation with application to robust fault detection, In Proc. of the American Control Conference, pp.4408-4412, San Diego, California, June 1999
5	P. Frank and X. Ding, Frequency domain approach to optimally robust residual generation and evaluation for model based fault diagnosis, Automatica, vol.30, no.5, pp.789-804, 1994 DOI ScienceOn
6	H.D. Tuan, P. Apkarian, T. Narikiyo and Y. Yamamoto, Parameterized linear matrix inequality techniques in fuzzy control system design, IEEE Trans. Fuzzy Systems, vol.9, no.2, April, pp.324-332, 2001 DOI ScienceOn
7	S.K.Nguang, P.Shi, Fuzzy output feedback control design for nonlinear systems : an LMI approach, IEEE Trans. Fuzzy Systems, vol.11, no.3, June pp.331-340, 2003 DOI ScienceOn
8	A. Rantzer and A. Megretsky, System analysis via integral quadratic constraints, In Proc. 33rd IEEE Conf. Decision Contr., pp.3062-3-67, Lake Buena Vista, FL, 1994
9	K. Tanaka, T. Ikeda and H. Wang, Robust stabilization of a class of uncertain nonlinear systems via fuzzy control : Quadratic stabilizability, control theory, and linear matrix inequalities, IEEE Trans. Fuzzy Systems, vol.4, no.1 , Feb., pp.1-13, 1996 DOI ScienceOn
10	J. Gertler, Analytical redundancy methods in fault detection and isolation, In Proc. IFAC/IMACS Symp. SAFEPROCESS'91, Baden-Baden 1991
11	J. Chen, R. Patton and H. Zhang, Design of unknown input observers and robust fault detection filters, Int. J. Control, vol.63, pp.85-105, 1996 DOI ScienceOn