Design of robust redundancy relations for a semi-scale YF-22 aircraft model

Abstract This paper presents an “ad hoc” methodology for the design of diagnostic software for the detection and isolation of faults on sensors and actuators of a remotely controlled semi scale YF-22 research aircraft. Starting from the structural analysis of the nonlinear dynamic equations of the aircraft, an algorithm, based on the “variables elimination method”, is proposed to compute a set of residual equations having all the possible fault signatures. The quality of each residual equation has been ranked according to a cost function chosen to represent implementation issues such as the sensitivity to measurement noise in the numerical computation of high order derivatives. An algorithm is then proposed for selecting a subset of residual equations with maximum “failure isolability” and minimum cost, according to the selected performance criteria. The issue of robustification of the residual equations to modeling errors and measurement noise has been addressed through nonlinear uncertainty mapping using Neural Networks in conjunction to FIR filters. The fault detection and isolation method has been applied by injecting simulated faults to flight data collected by a semi-scale YF-22 research aircraft model.

[1]  Mario G. Perhinschi,et al.  Learning-Based Sensor Validation Scheme Within Flight-Control Laws , 2004 .

[2]  Marcel Staroswiecki,et al.  Structural Analysis of Fault Isolability in the DAMADICS benchmark , 2006 .

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

[4]  Mattias Krysander,et al.  An Efficient Algorithm for Finding Over-constrained Sub-systems for Construction of Diagnostic Tests , 2005 .

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

[6]  M. Staroswiecki,et al.  A Structural Framework for the Design of FDI System in Large Scale Industrial Plants , 2000 .

[7]  Giampiero Campa,et al.  On‐line learning neural networks for sensor validation for the flight control system of a B777 research scale model , 2002 .

[8]  Marcel Staroswiecki,et al.  Analytical redundancy relations for fault detection and isolation in algebraic dynamic systems , 2001, Autom..

[9]  Linda Rattfält.  A comparative study of two structural methods for fault isolation analysis , 2004 .

[10]  Mogens Blanke,et al.  Cheap diagnosis using structural modelling and fuzzy-logic-based detection , 2003 .

[11]  Marcel Staroswiecki,et al.  Extension of Parity Space to Non Linear Polynomial Dynamic Systems , 1997 .

[12]  Niels Kjølstad Poulsen,et al.  Neural Networks for Modelling and Control of Dynamic Systems: A Practitioner’s Handbook , 2000 .

[13]  Michèle Basseville,et al.  Fault Detection and Isolation in Nonlinear Dynamic Systems: A Combined Input-Output and Local Approach , 1998, Autom..

[14]  Ron J. Patton,et al.  Fault detection and diagnosis in aerospace systems using analytical redundancy , 1991 .

[15]  Alberto Isidori,et al.  A geometric approach to nonlinear fault detection and isolation , 2000, IEEE Trans. Autom. Control..

[16]  J. Gauthier,et al.  Observability and observers for non-linear systems , 1986, 1986 25th IEEE Conference on Decision and Control.

[17]  Nilanjan Sarkar,et al.  Robust nonlinear analytic redundancy for fault detection and isolation in mobile robot , 2007, Int. J. Autom. Comput..

[18]  Marc Rauw,et al.  FDC 1.2 - A Simulink Toolbox for Flight Dynamics and Control Analysis , 2001 .

[19]  Paul M. Frank,et al.  Issues of Fault Diagnosis for Dynamic Systems , 2010, Springer London.

[20]  Erik Frisk,et al.  Lowering orders of derivatives in non-linear residual generation using realization theory , 2005, Autom..

[21]  J. Gertler.  Fault detection and isolation using parity relations , 1997 .

[22]  Marcello R. Napolitano,et al.  Design and flight-testing of non-linear formation control laws , 2007 .

[23]  Marcel Staroswiecki,et al.  Supervision of an industrial steam generator. Part I: Bond graph modelling , 2006 .

[24]  V. Cocquempot,et al.  Structural analysis for residual generation: towards implementation , 2004, Proceedings of the 2004 IEEE International Conference on Control Applications, 2004..