Hammerstein Model-Based Correlation UIO Method for Fault Detection of Nonlinear Flight Control Systems

In this paper, we propose an alternative approach – the correlation UIO method – for fault detection of nonlinear control systems. The approach exploits a property of the Hammerstein model with separable input process, in which case the cross-correlation function between the input process and the residual of a suitably designed linear UIO is decoupled from the nonlinearity. In an application to nonlinear flight-control systems, a Hammerstein model of the closed-loop system is obtained by a novel approach for system identification, in which the input-output correlation functions are used as data. To apply the correlation UIO method, a very weak (compared to disturbances) separable signal (sinusoid) is injected to the closed-loop system as a diagnostic signal. Simulation results based on an F-16 model show that the scheme is able to detect actuator lock-in-place fault even at trim deflection and in straight-level flight, which is the most difficult situation for flight-control fault detection. Moreover, the detection threshold is independent of the fault and control signals; under some assumptions, it can be arbitrarily increased by increasing the amplitude of the diagnostic signal. The simplicity and benefits of the proposed method are demonstrated through comparison with the standard linear UIO design.

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

[2]  M. Saif,et al.  Fault detection and isolation based on novel unknown input observer design , 2006, 2006 American Control Conference.

[3]  Kai-Yew Lum,et al.  Fault-tolerant flight tracking control with stuck faults , 2003, Proceedings of the 2003 American Control Conference, 2003..

[4]  G. Ducard,et al.  Extended Multiple Model Adaptive Estimation for the Detection of Sensor and Actuator Faults , 2005, Proceedings of the 44th IEEE Conference on Decision and Control.

[5]  Guillaume Ducard,et al.  Efficient Nonlinear Actuator Fault Detection and Isolation System for Unmanned Aerial Vehicles , 2008 .

[6]  Kwok Leung Lai,et al.  Identification of a Hammerstein model for wing flutter analysis using CFD data and correlation method , 2010, Proceedings of the 2010 American Control Conference.

[7]  Peter S. Maybeck,et al.  Multiple model adaptive estimation with filter spawning , 2002 .

[8]  Ali Saberi,et al.  Connections between H 2 optimal filters and unknown input observers – performance limitations of H 2 optimal filtering , 2004 .

[9]  Dan Wang,et al.  Adaptive unknown input observer approach for aircraft actuator fault detection and isolation , 2007 .

[10]  H. Marquez,et al.  Design of unknown input observers for Lipschitz nonlinear systems , 2005, Proceedings of the 2005, American Control Conference, 2005..

[11]  Mehrdad Saif,et al.  Adaptive Fault Detection for a Class of Nonlinear Systems Based on Output Estimator Design , 2008 .

[12]  Kwok Leung Lai,et al.  Reduced-Order Based Flutter Analysis for Complex Aeroelastic Systems , 2008 .

[13]  Frank L. Lewis,et al.  Aircraft Control and Simulation , 1992 .

[14]  D. N. Shields.  Models, residual design and limits to fault detection for a complex multi-tank hydraulic control system , 2003 .

[15]  Kemin Zhou,et al.  Fault Detection and Isolation for Nonlinear Systems with Full State Information , 2008 .

[16]  Stephen A. Billings,et al.  Identification of systems containing linear dynamic and static nonlinear elements , 1982, Autom..

[17]  Connections between H/sub 2/ optimal filters and unknown input observers performance limitations of H/sub 2/ optimal filtering , 2004, Proceedings of the 2004 American Control Conference.

[18]  Johan Schoukens,et al.  Hammerstein-Wiener system estimator initialization , 2002, Autom..

[19]  Chi-Tsong Chen,et al.  Linear System Theory and Design , 1995 .

[20]  Albert H. Nuttall.  Theory and application of the separable class of random processes , 1958 .

[21]  Ilya V. Kolmanovsky,et al.  Predictive energy management of a power-split hybrid electric vehicle , 2009, 2009 American Control Conference.

[22]  Paul M. Frank,et al.  ROBUST COMPONENT FAULT DETECTION AND ISOLATION IN NONLINEAR DYNAMIC SYSTEMS USING NONLINEAR UNKNOWN INPUT OBSERVERS , 1992 .

[23]  P. Frank,et al.  Survey of robust residual generation and evaluation methods in observer-based fault detection systems , 1997 .

[24]  D S Bernstein,et al.  Semiparametric identification of Wiener systems using a single harmonic input and retrospective cost optimization , 2010, Proceedings of the 2010 American Control Conference.

[25]  Dario H. Baldelli,et al.  Novel Nonlinear Hammerstein Model Identification: Application to Nonlinear Aeroelastic/Aeroaervoelastic System , 2008 .

[26]  J. Juang.  Applied system identification , 1994 .

[27]  F. Amato,et al.  Design of full order unknown input observers with H/sub /spl infin// performance , 2002, Proceedings of the International Conference on Control Applications.

[28]  Goutam Chakraborty,et al.  Robust Unknown Input Observer for Nonlinear Systems and Its Application to Fault Detection and Isolation , 2008 .

[29]  Youmin Zhang,et al.  Integrated active fault-tolerant control using IMM approach , 2001 .

[30]  Ai Poh Loh,et al.  A frequency domain approach for fault detection , 2008, Int. J. Control.

[31]  Ai Poh Loh,et al.  A novel UIO-based approach for fault detection and isolation in finite frequency domain , 2009, 2009 IEEE International Conference on Control and Automation.

[32]  S. Billings,et al.  Theory of separable processes with applications to the identification of nonlinear systems , 1978 .

[33]  Alexey Shumsky.  Algebraic Approach to the Problem of Fault Accommodation in Nonlinear Systems , 2008 .

[34]  S. Billings,et al.  Non-linear system identification using the Hammerstein model , 1979 .

[35]  Jun Xu,et al.  A gain-varying UIO approach with adaptive threshold for FDI of nonlinear F16 systems , 2010 .

[36]  Dario H. Baldelli,et al.  Nonlinear aeroelastic/aeroservoelastic modeling by block-oriented identification , 2005 .