Actuator fault diagnosis for affine nonlinear systems with unknown inputs

An actuator fault diagnosis approach for affine nonlinear systems with unknown inputs is presented. The proposed approach is based on an input/output relation involving not only the inputs and the outputs but also the higher derivatives of the outputs. It employs the recently developed high-order sliding-mode robust differentiators (HOSMRDs) to estimate the higher derivatives of the outputs. Under certain conditions, the proposed actuator fault diagnosis approach can be used to detect, isolate, and estimate actuator faults. A nonlinear system with an unknown input is used as an example, and simulation results show that the proposed actuator fault diagnosis approach works well in terms of fault detection, isolation and estimation.

[1]  Mehrdad Saif,et al.  Actuator fault isolation and estimation for uncertain nonlinear systems , 2005, 2005 IEEE International Conference on Systems, Man and Cybernetics.

[2]  Wen Chen,et al.  Observer-based strategies for actuator fault detection, isolation and estimation for certain class of uncertain nonlinear systems , 2007 .

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

[4]  Jie Chen,et al.  Design of unknown input observers and robust fault detection filters , 1996 .

[5]  A. Isidori Nonlinear Control Systems , 1985 .

[6]  Mehrdad Saif,et al.  A novel design for robust fault diagnostic observer , 1998, Proceedings of the 37th IEEE Conference on Decision and Control (Cat. No.98CH36171).

[7]  Chee Pin Tan,et al.  Sliding mode observers for fault detection and isolation , 2002 .

[8]  Dominique Sauter,et al.  Nonlinear filter design for fault diagnosis: application to the three-tank system , 2005 .

[9]  Mehrdad Saif,et al.  Nonlinear adaptive observer design for fault detection , 1995, Proceedings of 1995 American Control Conference - ACC'95.

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

[11]  M. Saif,et al.  Unknown input observer design for a class of nonlinear systems: an LMI approach , 2006, American Control Conference.

[12]  Paul M. Frank,et al.  Robust Component Fault Detection and Isolation in Nonlinear Dynamic Systems using Nonlinear unknown Input Observers , 1991 .

[13]  Mehrdad Saif,et al.  A new approach to robust fault detection and identification , 1993 .

[14]  M. Saif,et al.  High-order Sliding-mode Differentiator Based Actuator Fault Diagnosis For Linear Systems with Arbitrary Relative Degree and Unmatched Unknown Inputs , 2006, Proceedings of the 45th IEEE Conference on Decision and Control.

[15]  Paul M. Frank,et al.  Fault-diagnosis by disturbance decoupled nonlinear observers , 1991, [1991] Proceedings of the 30th IEEE Conference on Decision and Control.

[16]  James Lam,et al.  An LMI approach to design robust fault detection filter for uncertain LTI systems , 2003, Autom..

[17]  Rajesh Rajamani *,et al.  Sensor fault diagnostics for a class of non-linear systems using linear matrix inequalities , 2004 .

[18]  Christopher Edwards,et al.  Sliding mode observers for reconstruction of simultaneous actuator and sensor faults , 2003, 42nd IEEE International Conference on Decision and Control (IEEE Cat. No.03CH37475).

[19]  T. Floquet *,et al.  On the robust fault detection via a sliding mode disturbance observer , 2004 .

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

[21]  Alberto Isidori,et al.  A Geometric Approach to Nonlinear Fault Detection and Isolation , 2000 .

[22]  Hanlong Yang,et al.  State observation, failure detection and isolation (FDI) in bilinear systems , 1997 .

[23]  Yi Xiong,et al.  Robust and nonlinear fault diagnosis using sliding mode observers , 2001, Proceedings of the 40th IEEE Conference on Decision and Control (Cat. No.01CH37228).