A comparative study of NN- and EKF-based SFDA schemes with application to a nonlinear UAV model

References

• Alessandri , A , Caccia , M and Veruggio , G . 1999 . Fault Detection of Actuator Faults in Unmanned Underwater Vehicle . Control Engineering Practice , 7 : 357 – 368 .
   Web of Science ®Google Scholar
• An, Y. (1998), ‘A Design of Fault Tolerant Flight Control Systems for Sensor and Actuator Failures Using On-line Learning Neural Networks’, PhD thesis, Department of Mechanics and Aerospace Engineering, Morgantown, West Virginia.
   Google Scholar
• Buonanno, A., and Cook, M.V. (2005), ‘An Aerodynamic Simulation Model of the Eclipse UAV’, Internal Report No. FLAV-A01-002, Cranfield University, Bedford, UK.
   Google Scholar
• Campa , G , Fravolini , ML , Napolitano , M and Seanor , B . 2002 . Neural Networks Sensor Validation for the Flight Control System of a B777 Research Model . American Control Conference , 1 : 412 – 417 .
   Google Scholar
• Capriglione , D , Liguori , C and Pietrosanto , A . 2007 . Real Time Implementation of IDFIA Scheme in Automotive Systems . IEEE Transactions on Instrumentation and Measurement , 56 : 824 – 830 .
   Web of Science ®Google Scholar
• Chiang, C.Y., Juang, J.C. and Youssef, H. (1994), ‘Neural Network Approach to Aircraft Fault Detection, Isolation and Estimation Design’, AIAA-1994-273, AIAA 32nd Aerospace Sciences Meeting and Exhibit, Reno, NV, USA, 10–13 January 1994.
   Google Scholar
• Clark , RN and Setzer , W . 1980 . Sensor Fault Detection in a System with Random Disturbances . IEEE Transactions on Aerospace and Electronic Systems , 16 : 468 – 473 .
   Web of Science ®Google Scholar
• Cybenko , G . 1989 . Approximation by Superposition of Sigmoidal functions . Mathematics of Control, Signals, and Systems , 2 : 303 – 309 .
   Google Scholar
• Gallinari , F . 1998 . Industrial Applications of Neural Networks , Great Britain : World Scientific Publishing Co. Pte. Ltd. .
   Google Scholar
• Haykin , S . 1994 . Neural Networks: A Comprehensive Foundation , New York : Macmillan College Publishing Company .
   Google Scholar
• Heredia, G., Ollero, A., Mahtani, R., Remub, V., and Mausial, M. (2005), ‘Detection of Sensor Faults in Autonomous Helicopters’, in Proceedings of the 2005 IEEE International Conference on Robotics and Automation. Barcelona, Spain, pp. 2229–2234.
   Google Scholar
• Hornik , K , Stinchcombe , M and White , H . 1989 . Multilayer Feedforward Networks are Universal Approximators . Neural Networks , 2 : 359 – 366 .
   Web of Science ®Google Scholar
• Huang, W., Wang, K., and Chen, Q. (1996), ‘Sensor Failure Detection and Data Recovery Based on Neural Network’, AIAA-1996-2932, AIAA, ASME, SAE and ASEE, 32nd Joint Propulsion Conference and Exhibit, Lake Buena Vista, FL, USA, 1–3 July 1996.
   Google Scholar
• Isermann , R . 1984 . Process Fault Detection Based on Modelling and Estimation Methods: A survey . Automatica , 20 : 387 – 404 .
   Web of Science ®Google Scholar
• Isermann , R and Balle , P . 1997 . Trends in the Application of Model-based Fault Detection and Diagnosis of Technical Processes . Control Engineering Practice , 5 : 709 – 719 .
   Web of Science ®Google Scholar
• Kadirkamanathan , V and Niranjan , M . 1993 . A Function Estimation Approach to Sequential Learning with Neural Networks . Neural Computatation , 5 : 954 – 975 .
   Web of Science ®Google Scholar
• Kalman , RE . 1960 . A New Approach to Linear Filtering and Prediction Problems . Journal of Basic Engineering , 82 : 35 – 45 .
   Google Scholar
• Li , Y , Sundararajan , N and Saratchandran , P . 2000 . Analysis of Minimal Radial Basis Function Network Algorithm for Real Time Identification of Nonlinear Dynamic Systems . IEEE Control Theory and Application , 4 : 476 – 484 .
   Web of Science ®Google Scholar
• Mattern, D., Jaw, L., Guo, T.H., Graham, R., and McCoy, W. (1998), ‘Using Neural Networks for Sensor Validation’, AIAA-1998-3547.
   Google Scholar
• Mehra , RK and Peschon , J . 1971 . An Innovative Approach to Fault Detection and Diagnosis in Dynamic Systems . Automatica , 7 : 637 – 643 .
   Web of Science ®Google Scholar
• Moller, J., Litt, J., and Guo, T.H. (1998), ‘Neural Network Based Sensor Validation for Turboshaft Engines’, AIAA-1998-3605, AIAA, ASME, SAE and ASEE, 34th Joint Propulsion Conference and Exhibit, Cleverland, OH, USA, 13–15 July 1998.
   Google Scholar
• Motyka, P., Bonnice, W., Hall, S., and Wagner, E. (1985), ‘The Evaluation of Failure Detection and Isolation Algorithms for Restructurable Control’, NASA Contractor Report No. 177983.
   Google Scholar
• Napolitano, M., Neppach, C., Casdorph, V., and Innocenti, M. (1994), ‘Sensor Failure Detection, Identification and Accommodation Using Online Learning Neural Architectures’, AIAA Report No. AIAA-1994-3598, AIAA Guidance, Navigation and Control Conference, Scottsdale, AZ, USA, 1–3 August 1994.
   Google Scholar
• Napolitano , M , Neppach , C , Casdorph , V and Innocenti , M . 1995 . Neural Network Based Scheme for Sensor Failure Detection, Identification and Accommodation . AIAA Journal of Guidance Control and Dynamics , 18 : 1280 – 1286 .
   Web of Science ®Google Scholar
• Napolitano , M , An , Y and Seanor , B . 2000 . A Fault Tolerant Flight Control System for Sensor and Actuator Failures Using Neural Networks . Aircraft Design , 3 : 103 – 128 .
   Google Scholar
• Napolitano, N., An, Y., Seanor, B., Pispistos, S., and Martinelli, D. (1999), ‘Application of a Neural Sensor Validation Scheme to Actual Boeing B737 Flight Data’, in Proceedings of the AIAA Guidance Navigation and Control Conference, pp. 139–147.
   Google Scholar
• Napolitano, M., Windon, D., Casanova, J., and Innocenti, M. (1996), ‘A Comparison Between Kalman Filter and Neural Network Approaches for Sensor Validation’, AIAA Report No. AIAA-1996-3894, AIAA Guidance, Navigation and Control Conference, San Diego, CA, USA, 29–31 July 1996.
   Google Scholar
• Neppach, C., and Casdorph, V. (1995), ‘Sensor Failure Detection, Identification and Accommodation in a System Without Sensor Redundancy’, AIAA Report No. AIAA-1995-11, AIAA 33rd Aerospace Sciences Meeting and Exhibit, Reno, NV, 9–12 January 1995.
   Google Scholar
• Perla , R , Mukhopadhyay , S and Samanta , A . 2004 . Sensor Fault Detection and Isolation Using Artificial Neural Networks . IEEE Region Conference TENCON , 4 : 676 – 679 .
   Google Scholar
• Powell , MJD . 1987 . “ Radial Basis Function for Multivariable Interpolation: A Review ” . In Algorithms for Approximation , Edited by: Mason , JC and Cox , MG . 143 – 167 . Oxford, UK : Clarendon Press .
   Google Scholar
• Samy , I , Postlethwaite , I and Gu , D-W . 2008a . “ Neural Network Sensor Validation Scheme Demonstrated on a UAV Model ” . In in IEEE Proceedings of CDC , 1237 – 1242 . Cancun : Mexico .
   Google Scholar
• Samy , I , Postlethwaite , I and Gu , D-W . 2008b . “ Detection of Additive Sensor Faults in a UAV Model Using Neural Networks ” . In in Proceedings of UKACC , Tu.05.05 UK : Manchester .
   Google Scholar
• Sheng, W., Giampiero, C., Napolitano, M., and Fravolini, M. (2002), ‘Online Learning RBF Neural Networks for Sensor Validation’, AIAA Report No. AIAA-2002-4996, 2002, AIAA Guidance, Navigation and Control Conference and Exhibit, Monterey, CA, USA, 5–8 August 2002.
   Google Scholar
• Sorenson , HW . 1985 . Kalman Filtering: Theory and Application , New York : IEEE Press .
   Google Scholar
• Trammel , C , Vossler , G and Feldmann , M . 1997 . UK Ministry of Defence Generic Health and Usage Monitoring System (GenHUMS) . Journal of Aircraft Engineering and Aerospace Technology , 69 : 414 – 422 .
   Web of Science ®Google Scholar