Open Access Open Access  Restricted Access Subscription Access

Robust Model Reference Fault Detection and Identification System for Fixed Wing Aircrafts


Affiliations
1 Vel Tech Rangarajan Dr.Sagunthala R and D Institute of Science and Technology, Chennai, India
 

   Subscribe/Renew Journal


Fault Detection and Identification system (FDI) and Fault Tolerant Flight Control (FTFC) system are used to correct the faulty operation of an aircraft. Both FDIs and FTFCs have operational disadvantages due to their inherent limitation of fault source identification. This paper presents the design and implementation of a robust model reference fault detection and identification (MRFDI) system on a fixed-wing aircraft for identifying actuator fault, instrument fault and presence of any uncertainties. The proposed MRDFI fuses the real-time parameters and actuator feedback to combine the advantages of data driven and model reference FDI that makes robust fault estimation. The MRFDI system is implemented on a typical aircraft altitude hold autopilot simulation environment with a predefined fault scenario. The fault scenario includes a faulty elevator, a faulty skin-implantable sensor and wind gust as environmental uncertainty. The MRFDI performs logical analysis to detect fault using state-dependent real-time parameters and state-independent skin implantable sensor. This two-step fault detection method makes MRFDI robust to any type of fault identification. The results show that the MRFDI detects and distinguishes faults in actuator, instrument and any of the listed uncertainties thrown by the environment accurately.

Keywords

Model Reference Fault Detection and Identification System, Real-Time System Identification, Autopilot.
User
Subscription Login to verify subscription
Notifications
Font Size

  • G. Chowdhary, E.N. Johnson, R. Chandramohan, M.S. Kimbrell and A. Calise. 2013. Guidance and control of airplanes under actuator failures and severe structura l damage, J. Guidance, Control, and Dynamics, 36(4) , 1093-1104. https://doi.org/10.2514/1.58028.
  • G. Chowdhary, W.D. Busk and E. Johnson. 2010. Realtime system identification of a small multi-engine aircraft with structural damage, AIAA Paper 2010-3472. https://doi.org/10.2514/6.2010-3472.
  • D. Jourdan, M. Piedmonte, V. Gavrilets, D. Vos and J.M. Cormick. 2010. Enhancing UAV survivability through damage tolerant control, Proc. AIAA Guidance, Navigation, and Control Conf., Toronto, Canada. https://doi.org/10.2514/6.2010-7548.
  • X. Wang, S. Wang, Z. Yang and C. Zhang. 2015. Active fault-tolerant control strategy of large civil aircraft under elevator failures, Chinese J. Aeronautics, 28(6), 1658-1666. https://doi.org/10.1016/j.cja.2015.10.001.
  • D.D. Dhadekar and S.E. Talole. 2018. Robust fault tolerant longitudinal aircraft control, IFAC-Papers on Line, 51(1), 604-609. https://doi.org/10.1016/j.ifacol.2018.05.101.
  • Y. Zhang and J. Jiang. 2003. Bibliographical review on reconfigurable fault-tolerant control systems, Proc. 5th IFAC Symp. Fault Detection, Supervision and Safety for Technical Processes, 265-276. https://doi.org/10.1016/S1474-6670(17)36503-5.
  • X. Yu and Y. Zhang. 2015. Design of passive faulttolerant flight controller against actuator failures, Chinese J. Aeronautics, 28(1), 180-190. https://doi.org/10.1016/j.cja.2014.12.006.
  • J. Wang, S. Wang, X. Wang, C. Shi and M.M. Tomovic. 2016. Active fault tolerant control for vertical tail damaged aircraft with dissimilar redundant actuation system, Chinese J. Aeronautics, 29(5), 1313-1325. https://doi.org/10.1016/j.cja.2016.08.009.
  • Q.U. Liang, L.I. Yinghui, X.U. Haojun, D. Zhang and Y.U.A.N. Guoqiang. 2017. Aircraft nonlinear stability analysis and multidimensional stability region estimation under icing conditions, Chinese J. Aeronautics, 30(3), 976-982. https://doi.org/10.1016/j.cja.2017.02.003.
  • X. Wang, C. Xiang, H. Najjaran, B. Xu. 2018. Robust adaptive fault-tolerant control of a tandem coaxial ducted fan aircraft with actuator saturation, Chinese J. Aeronautics, 31(6), 1298-1310. https://doi.org/10.1016/j.cja.2018.03.018.
  • R. Khan, P. Williams, P. Riseborough, A. Rao and R. Hill. 2016. Active fault tolerant flight control system design, arXiv:1610.02139v1, 1-31.
  • O.N. Korsun, M.H. Om, K.Z. Latt and A.V. Stulovskii. 2017. Real-time aerodynamic parameter identification for the purpose of aircraft intelligent technical state monitoring, Proc. Computer Sci., 103, 67-74. https://doi.org/10.1016/j.procs.2017.01.014.
  • O.N. Korsun, B.K. Poplavsky and S.J. Prihodko. 2017. Intelligent support for aircraft flight test data processing in problem of engine thrust estimation, Proc. Computer Sci., 103, 82-87. https://doi.org/10.1016/j.procs.2017.01.017.
  • A. Dorobantu, A. Murch, B. Mettler and G. Balas. 2013. System identification for small, low-cost, fixed-wing unmanned aircraft, J. Aircraft, 50(4), 1117-1130. https://doi.org/10.2514/1.C032065.
  • J.A. Grauer. 2016. Parameter uncertainty for aircraft aerodynamic modeling using recursive least squares, Proc. AIAA Atmospheric Flight Mechanics Conf., San Diego, California, USA. https://doi.org/10.2514/6.2016-2009.
  • H. Pfifer and B.P. Danowsky. 2016. System Identification of a small flexible aircraft-invited, Proc. AIAA Atmospheric Flight Mechanics Conf., San Diego, California, USA. https://doi.org/10.2514/6.2016-1750.
  • R.C. Nelson. 1989. Flight Stability and Automatic Control, McGraw-Hill, New York.
  • B. Etkin and L.D. Reid. 1996. Dynamics of Flight: Stability and Control, 3rd Ed., New York.
  • R.V. Jategaonkar. 2015. Flight Vehicle System Identification: A Time-Domain Methodology, AIAA. https://doi.org/10.2514/4.102790.
  • N. Bayar, S. Darmoul, S.H. Gabouj and H. Pierreval. 2015. Fault detection, diagnosis and recovery using artificial immune systems: A review, Engg. Applications of Artificial Intelligence, 46, 43-57. https://doi.org/10.1016/j.engappai.2015.08.006.

Abstract Views: 329

PDF Views: 116




  • Robust Model Reference Fault Detection and Identification System for Fixed Wing Aircrafts

Abstract Views: 329  |  PDF Views: 116

Authors

R. Jaganraj
Vel Tech Rangarajan Dr.Sagunthala R and D Institute of Science and Technology, Chennai, India
R. Velu
Vel Tech Rangarajan Dr.Sagunthala R and D Institute of Science and Technology, Chennai, India

Abstract


Fault Detection and Identification system (FDI) and Fault Tolerant Flight Control (FTFC) system are used to correct the faulty operation of an aircraft. Both FDIs and FTFCs have operational disadvantages due to their inherent limitation of fault source identification. This paper presents the design and implementation of a robust model reference fault detection and identification (MRFDI) system on a fixed-wing aircraft for identifying actuator fault, instrument fault and presence of any uncertainties. The proposed MRDFI fuses the real-time parameters and actuator feedback to combine the advantages of data driven and model reference FDI that makes robust fault estimation. The MRFDI system is implemented on a typical aircraft altitude hold autopilot simulation environment with a predefined fault scenario. The fault scenario includes a faulty elevator, a faulty skin-implantable sensor and wind gust as environmental uncertainty. The MRFDI performs logical analysis to detect fault using state-dependent real-time parameters and state-independent skin implantable sensor. This two-step fault detection method makes MRFDI robust to any type of fault identification. The results show that the MRFDI detects and distinguishes faults in actuator, instrument and any of the listed uncertainties thrown by the environment accurately.

Keywords


Model Reference Fault Detection and Identification System, Real-Time System Identification, Autopilot.

References





DOI: https://doi.org/10.4273/ijvss.10.5.14