LMI-based Lipschitz Observer Design with Application in Fault Diagnosis

The problem of fault detection and diagnosis in the class of nonlinear Lipschitz systems is considered. An observer-based approach offering extra degrees of freedom over classical Lipschitz observers is introduced. This freedom is used for the sensor faults diagnosis problem with the objective to make the residual converge to the faults vector achieving detection and estimation at the same time. The use of appropriate weightings to solve this problem in a standard convex optimization framework is also demonstrated. A LMI design procedure solvable using commercially available software is presented

[1]  G. Sallet,et al.  Observers for Lipschitz non-linear systems , 2002 .

[2]  Richard Vernon Beard,et al.  Failure accomodation in linear systems through self-reorganization. , 1971 .

[3]  S. H. Zak,et al.  On the stabilization and observation of nonlinear/uncertain dynamic systems , 1990, IEEE 1989 International Conference on Systems Engineering.

[4]  Marcel Staroswiecki,et al.  Fault Accommodation for Nonlinear Dynamic Systems , 2006, IEEE Transactions on Automatic Control.

[5]  R. Patton,et al.  Observer Design for a Class of Non-Linear Systems , 1997 .

[6]  Hong Wang,et al.  Design of fault diagnosis filters and fault-tolerant control for a class of nonlinear systems , 2001, IEEE Trans. Autom. Control..

[7]  Qing Zhao,et al.  H/sub /spl infin// observer design for lipschitz nonlinear systems , 2006, IEEE Transactions on Automatic Control.

[8]  P. Gahinet,et al.  A linear matrix inequality approach to H∞ control , 1994 .

[9]  J. Hedrick,et al.  Observer design for a class of nonlinear systems , 1994 .

[10]  Jie Chen,et al.  Robust Model-Based Fault Diagnosis for Dynamic Systems , 1998, The International Series on Asian Studies in Computer and Information Science.

[11]  Alan S. Willsky,et al.  A survey of design methods for failure detection in dynamic systems , 1976, Autom..

[12]  R. Rajamani Observers for Lipschitz nonlinear systems , 1998, IEEE Trans. Autom. Control..

[13]  Dingli Yu,et al.  A bilinear fault detection observer , 1996, Autom..

[14]  Arun T. Vemuri,et al.  Sensor bias fault diagnosis in a class of nonlinear systems , 2001, IEEE Trans. Autom. Control..

[15]  A. Zolghadri An algorithm for real-time failure detection in Kalman filters , 1996, IEEE Trans. Autom. Control..

[16]  J. Doyle,et al.  Essentials of Robust Control , 1997 .

[17]  Hassan Hammouri,et al.  Observer-based approach to fault detection and isolation for nonlinear systems , 1999, IEEE Trans. Autom. Control..

[18]  D. Maquin,et al.  Non-linear observer-based fault detection , 1994, 1994 Proceedings of IEEE International Conference on Control and Applications.

[19]  A. Pertew,et al.  H ∞ synthesis of unknown input observers for non-linear Lipschitz systems , 2005 .

[20]  F. Thau Observing the state of non-linear dynamic systems† , 1973 .

[21]  P. Frank,et al.  Deterministic nonlinear observer-based approaches to fault diagnosis: A survey , 1997 .

[22]  R. K. Mehra,et al.  Correspondence item: An innovations approach to fault detection and diagnosis in dynamic systems , 1971 .

[23]  Alain Glumineau,et al.  Direct transformation of nonlinear systems into state affine MISO form for observer design , 2003, IEEE Trans. Autom. Control..

[24]  Steven X. Ding,et al.  Model-based fault diagnosis in technical processes , 2000 .

[25]  Jie Chen,et al.  Observer-based fault detection and isolation: robustness and applications , 1997 .

[26]  Jiang Bin Fault accommodation for a class of nonlinear systems based on neural network observer , 2007 .