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Robust fault diagnosis scheme in a class of nonlinear system based on UIO and fuzzy residual

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  • Control Theory and Applications
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Abstract

In this study, a novel robust fault diagnosis scheme is developed for a class of nonlinear systems when both fault and disturbance are considered. The proposed scheme includes both component and sensor fault with nonlinear system that transferred to nonlinear Takagi-Sugeno (T-S) model. It considers a larger category of nonlinear system when fuzzification is used for only nonlinear distribution matrices. In fact the proposed method covers nonlinear systems could not transform to linear T-S model. This paper studies the problem of robust fault diagnosis based on two fuzzy nonlinear observers, the first one is a fuzzy nonlinear unknown input observer (FNUIO) and the other is a fuzzy nonlinear Luenberger observer (FNLO). This approach decouples the faulty subsystem from the rest of the system through a series of transformations. Then, the objective is to design FNUIO to guarantee the asymptotic stability of the error dynamic using the Lyapunov method; meanwhile, FNLO is designed for faulty subsystem to generate fuzzy residual signal based on a quadratic Lyapunov function and some matrices inequality convexification techniques. FNUIO affects only the fault free subsystem and completely removes any unknown inputs such as disturbances when residual signal is generated by FNLO is affected by component or sensor fault. This novelty and using nonlinear system in T-S model make the proposed method extremely effective from last decade literature. Sufficient conditions are established in order to guarantee the convergence of the state estimation error. Thus, a residual generator is determined on the basis of LMI conditions such that the estimation error is completely sensitive to fault vector and insensitive to the unknown inputs. Finally, an numerical example is given to show the highly effectiveness of the proposed fault diagnosis scheme.

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Author information

Authors and Affiliations

  1. Department of Electrical and Computer Engineering, Science and Research branch, Islamic Azad University, Tehran, Iran

    S. Hamideh Sedigh Ziyabari

  2. Faculty of Electrical Engineering, Department of Mechatronics Engineering, K. N. Toosi University of Technology, Tehran, Iran

    Mahdi Aliyari Shoorehdeli

Authors
  1. S. Hamideh Sedigh Ziyabari
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  2. Mahdi Aliyari Shoorehdeli
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Correspondence to S. Hamideh Sedigh Ziyabari.

Additional information

Recommended by Associate Editor Guang-Hong Yang under the direction of Editor Euntai Kim.

S. Hamideh Sedigh Ziyabari received the B.S. degree in Electronic Engineering from Guilan University in 2008 and M.Sc degree in Control Engineering from the Sahand University of Technology, Tabriz, Iran in 2010. In Sep.2012, She joined as a PhD student to Control and System of Science and Research branch of Azad University of Tehran. Her research interests include Fault detection and estimation, Nonlinear control, Predictive control and their application to control of the world.

Mahdi Aliyari Shoorehdeli received his B.S. degree in Electronic Engineering in 2001, his M.Sc and Ph.D. degrees in Control Engineering in 2003 and 2008 in Control Engineering from K. N. Toosi University of Technology, Tehran repetitively. He has been with the Mechatronics Department of K. N. Toosi University of Technology, Tehran, Iran since 2010. His research interests include Fault Diagnosis, System Identification and Alarm Management.

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Ziyabari, S.H.S., Shoorehdeli, M.A. Robust fault diagnosis scheme in a class of nonlinear system based on UIO and fuzzy residual. Int. J. Control Autom. Syst. 15, 1145–1154 (2017). https://doi.org/10.1007/s12555-016-0145-0

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