Fault diagnosis of nonlinear systems using higher order sliding mode technique

This paper presents a synthesis of fault diagnosis method for nonlinear systems through the parameter estimation using higher order sliding modes. Initially the uncertain parameters of nonlinear system using robust exact differentiator are estimated. Then residual signal is reconstructed using the estimated parameters, inputs, outputs and their estimated derivatives. The novelty of the method is the determination of unknown, uncertain system parameters by calculating accurate derivatives of the measured system inputs and outputs. To validate the technique, simulations have been performed for a uncertain nonlinear Three Tank System. The convergence in this approach is shown to be faster than similar existing techniques by an order of magnitude even in the presence of measurement noise. The proposed fault diagnosis technique requires no prior information about uncertain/unknown process parameters and statistical knowledge of process.

[1]  Christopher Edwards,et al.  New result in robust actuator fault reconstruction with application to an aircraft , 2007, 2007 IEEE International Conference on Control Applications.

[2]  M. Anguelova.  Observability and identifiability of nonlinear systems with applications in biology , 2007 .

[3]  Christian Bohn.  Recursive parameter estimation for nonlinear continuous time systems through sensitivity model based adaptive filters , 2000 .

[4]  A. Levant.  Robust exact differentiation via sliding mode technique , 1998 .

[5]  Halim Alwi,et al.  Fault Detection and Fault-Tolerant Control of a Civil Aircraft Using a Sliding-Mode-Based Scheme , 2008, IEEE Transactions on Control Systems Technology.

[6]  Rolf Isermann,et al.  Model-based fault-detection and diagnosis - status and applications , 2004, Annu. Rev. Control..

[7]  Chrissanthi Angeli,et al.  On-Line Fault Detection Techniques for Technical Systems: A Survey , 2004, Int. J. Comput. Sci. Appl..

[8]  Sarah K. Spurgeon,et al.  Sliding Mode Observers for Fault Detection , 1997 .

[9]  Christopher Edwards,et al.  Sliding mode observers for robust detection and reconstruction of actuator and sensor faults , 2003 .

[10]  J.J. Gertler,et al.  Survey of model-based failure detection and isolation in complex plants , 1988, IEEE Control Systems Magazine.

[11]  Arie Levant,et al.  Exact Differentiation of Signals with Unbounded Higher Derivatives , 2006, Proceedings of the 45th IEEE Conference on Decision and Control.

[12]  Cédric Join,et al.  CONTROL OF AN UNCERTAIN THREE-TANK SYSTEM VIA ON-LINE PARAMETER IDENTIFICATION AND FAULT DETECTION , 2005 .

[13]  Wen Chen,et al.  Design of Sliding Mode Observers with Sensitivity to Incipient Faults , 2007, 2007 IEEE International Conference on Control Applications.

[14]  Fliess Michel,et al.  Control via state estimations of some nonlinear systems , 2004 .

[15]  Arie Levant,et al.  Higher-order sliding modes, differentiation and output-feedback control , 2003 .

[16]  H. K. Khalil,et al.  Numerical differentiation using high-gain observers , 1997, Proceedings of the 36th IEEE Conference on Decision and Control.

[17]  D. Thomasset,et al.  A Robust Differentiator-Controller Design for an Electropneumatic System , 2005, Proceedings of the 44th IEEE Conference on Decision and Control.

[18]  Yacine Chitour,et al.  Time-varying high-gain observers for numerical differentiation , 2002, IEEE Trans. Autom. Control..

[19]  Tao Jiang,et al.  Parameter Estimation-Based Fault Detection, Isolation and Recovery for Nonlinear Satellite Models , 2008, IEEE Transactions on Control Systems Technology.