Neural Network Based Icing Identification and Fault Tolerant Control of a 340 Aircraft
This paper presents a Neural Network (NN) identification of icing parameters in an A340 aircraft and a reconfiguration technique to keep the A/C performance close to the performance prior to icing. Five aircraft parameters are assumed to be considerably affected by icing. The off-line training for identifying the clear and iced dynamics is based on the Levenberg-Marquard Backpropagation algorithm. The icing parameters are located in the system matrix. The physical locations of the icing are assumed at the right and left wings. The reconfiguration is based on the technique known as the control mixer approach or pseudo inverse technique. This technique generates the new control input vector such that the A/C dynamics is not much affected by icing. In the simulations, the longitudinal and lateral dynamics of an Airbus A340 aircraft model are considered, and the stability derivatives affected by icing are identified. The simulation results show the successful NN identification of the icing parameters and the reconfigured flight dynamics having the similar performance before the icing. In other words, the destabilizing icing affect is compensated.
[1] Ratvasky, T. P. and Ranaudo, R. J. (1993). Icing Effects on Aircraft
Stability and Control Determined from Flight Data - Preliminary
Results, NASA TM-105977 (AIAA-93-0398, 31st Aerospace Sciences
Meeting and Exhibit), January.
[2] Bragg, M.B., Perkins, W.R., Sarter, N.B., Başar, T., Voulgaris, P.G.,
Gurbacki, H.M., Melody, J.W., and McCray, S.A. (1998). An
interdisciplinary approach to in-flight aircraft icing safety, in Proc. 36th
AIAA Aerospace Sciences Meeting and Exhibit, Reno, NV, AIAA-98-
0095.
[3] Bragg, M. B., Perkins, W.R., Basar, T., Sarter, N. B., Voulgaris, P. G.,
Selig, M., and Melody, J. (2002). Smart Icing Systems for Aircraft Icing
Safety, Reno NV, AIAA-2002-0813.
[4] Miller, R.H. and Ribbens, W.B. (1999). The Effects of Icing on the
Longitudinal Dynamics of an Icing Research Aircraft. Number 99-0636
in 37th Aerospace Sciences. AIAA, January.
[5] Ratvasky, T. P. and van Zante, J. F. (1999), In-Flight Aerodynamic
Measurements of an Iced Horizontal Tailplane, AIAA-99-0638, 37th
Aerospace Sciences Meeting and Exhibit, January.
[6] Bragg, M.B., Hutchison, T., Oltman, R., Pokhariyal, D. and Merritt, J.
(2000). Effect of ice accretion on aircraft flight dynamics, in Proc. 38th
AIAA Aerospace Sciences Meeting and Exhibit, Reno, NV, AIAA-2000-
0360.
[7] Melody, J.W., Başar, T., Perkins, W.R. and Voulgaris, P. G. (2000). Hinfinity
Parameter identification for inflight detection of aircraft icing,
Control Engineering Practice, vol. 8, pp. 985-1001, Sept. 2000.
[8] Melody, J.W., Hillbrand, T., Başar, T., Perkins, W.R. (2001). H-Infinity
Parameter Identification for In-flight Detection of Aircraft Icing: The
Time Varying Case, IFAC Control Engineering Practice, 1327-1335.
[9] Schuchard, E. A., Melody, J. W., Başar, T., Perkins, W. R., and
Voulgaris, P. (2000). Detection and classification of aircraft icing using
neural networks, in Proc. 38th AIAA Aerospace Sciences Meeting and
Exhibit, no. AIAA-2000-0361, (Reno, NV), Jan. 2000.
[10] Johnson, M.D., Rokhsaz, K. (2000). Using Artificial Neural Networks
and Self Organizing Maps for Detection of Airframe Icing, The 2000
Atmospheric Flight Mechanics Conference, AIAA-2000-4099.
[11] Myers, T.T., Klyde, D.H., Magdaleno, R.E. (2000). The Dynamic Icing
Detection System, 38th AIAA Aerospace Sciences Meeting and Exhibit,
Reno, NV.
[12] Rattan K.S. (1985). Reconfiguration of flight control systems after
effector failure. Proceedings of Fourth International Conference on
System Engineering, Coventry Polytechnic UK.
[13] Hajiyev C.M. and Caliskan F. (2001). Integrated sensor/actuator FDI
and reconfigurable control for fault-tolerant flight control system design.
The Aeronautical Journal, the Royal Aeronautical Society, England,
September, pp. 525-533.
[14] Aykan, R., (2005). Aircraft icing detection, identification and
reconfigurable control based on Kalman filtering and neural networks
PhD. Thesis, Institute of Science and Technology, Istanbul Technical
University.
[15] Roskam, J., (1982). Airplane Flight Dynamics and Automatic Flight
Controls. Part I and II, Roskam Aviation and Engineering Corporation,
Kansas, USA.
[16] Advanced Aircraft Analysis, Version 2.0, Software, (1997). Design,
Analysis and Research (DAR) Corporation, Kansas, USA.
[17] Melody, J.W., Pokhariyal, D., Merret, J., Başar, T., Bragg, M.B. (2001).
Sensor Integration for In-flight Icing Characterization Using Neural
Networks, 39th Aerospace Science Meeting and Exhibit, Reno, Nevada,
AIAA-2001-0542.
[18] McLean, D. (1990). Automatic Flight Control Systems, Prentice Hall
International Ltd. London, UK.
[19] Campa, G., Fravolini, M.L., Napolitano, M.R. (2002) A Library of
Adaptive Neural Networks for Control Purposes, IEEE International
Symposium on Computer Aided Control System Design, Glasgow,
Scotland, UK.
[20] Yang, Z. and Blanke, M. (2000). Robust Control Mixer Module Method
for Control Reconguration. Proc. of American Control Conference,
Chicago, Illinois, pp. 3407-3411.
[1] Ratvasky, T. P. and Ranaudo, R. J. (1993). Icing Effects on Aircraft
Stability and Control Determined from Flight Data - Preliminary
Results, NASA TM-105977 (AIAA-93-0398, 31st Aerospace Sciences
Meeting and Exhibit), January.
[2] Bragg, M.B., Perkins, W.R., Sarter, N.B., Başar, T., Voulgaris, P.G.,
Gurbacki, H.M., Melody, J.W., and McCray, S.A. (1998). An
interdisciplinary approach to in-flight aircraft icing safety, in Proc. 36th
AIAA Aerospace Sciences Meeting and Exhibit, Reno, NV, AIAA-98-
0095.
[3] Bragg, M. B., Perkins, W.R., Basar, T., Sarter, N. B., Voulgaris, P. G.,
Selig, M., and Melody, J. (2002). Smart Icing Systems for Aircraft Icing
Safety, Reno NV, AIAA-2002-0813.
[4] Miller, R.H. and Ribbens, W.B. (1999). The Effects of Icing on the
Longitudinal Dynamics of an Icing Research Aircraft. Number 99-0636
in 37th Aerospace Sciences. AIAA, January.
[5] Ratvasky, T. P. and van Zante, J. F. (1999), In-Flight Aerodynamic
Measurements of an Iced Horizontal Tailplane, AIAA-99-0638, 37th
Aerospace Sciences Meeting and Exhibit, January.
[6] Bragg, M.B., Hutchison, T., Oltman, R., Pokhariyal, D. and Merritt, J.
(2000). Effect of ice accretion on aircraft flight dynamics, in Proc. 38th
AIAA Aerospace Sciences Meeting and Exhibit, Reno, NV, AIAA-2000-
0360.
[7] Melody, J.W., Başar, T., Perkins, W.R. and Voulgaris, P. G. (2000). Hinfinity
Parameter identification for inflight detection of aircraft icing,
Control Engineering Practice, vol. 8, pp. 985-1001, Sept. 2000.
[8] Melody, J.W., Hillbrand, T., Başar, T., Perkins, W.R. (2001). H-Infinity
Parameter Identification for In-flight Detection of Aircraft Icing: The
Time Varying Case, IFAC Control Engineering Practice, 1327-1335.
[9] Schuchard, E. A., Melody, J. W., Başar, T., Perkins, W. R., and
Voulgaris, P. (2000). Detection and classification of aircraft icing using
neural networks, in Proc. 38th AIAA Aerospace Sciences Meeting and
Exhibit, no. AIAA-2000-0361, (Reno, NV), Jan. 2000.
[10] Johnson, M.D., Rokhsaz, K. (2000). Using Artificial Neural Networks
and Self Organizing Maps for Detection of Airframe Icing, The 2000
Atmospheric Flight Mechanics Conference, AIAA-2000-4099.
[11] Myers, T.T., Klyde, D.H., Magdaleno, R.E. (2000). The Dynamic Icing
Detection System, 38th AIAA Aerospace Sciences Meeting and Exhibit,
Reno, NV.
[12] Rattan K.S. (1985). Reconfiguration of flight control systems after
effector failure. Proceedings of Fourth International Conference on
System Engineering, Coventry Polytechnic UK.
[13] Hajiyev C.M. and Caliskan F. (2001). Integrated sensor/actuator FDI
and reconfigurable control for fault-tolerant flight control system design.
The Aeronautical Journal, the Royal Aeronautical Society, England,
September, pp. 525-533.
[14] Aykan, R., (2005). Aircraft icing detection, identification and
reconfigurable control based on Kalman filtering and neural networks
PhD. Thesis, Institute of Science and Technology, Istanbul Technical
University.
[15] Roskam, J., (1982). Airplane Flight Dynamics and Automatic Flight
Controls. Part I and II, Roskam Aviation and Engineering Corporation,
Kansas, USA.
[16] Advanced Aircraft Analysis, Version 2.0, Software, (1997). Design,
Analysis and Research (DAR) Corporation, Kansas, USA.
[17] Melody, J.W., Pokhariyal, D., Merret, J., Başar, T., Bragg, M.B. (2001).
Sensor Integration for In-flight Icing Characterization Using Neural
Networks, 39th Aerospace Science Meeting and Exhibit, Reno, Nevada,
AIAA-2001-0542.
[18] McLean, D. (1990). Automatic Flight Control Systems, Prentice Hall
International Ltd. London, UK.
[19] Campa, G., Fravolini, M.L., Napolitano, M.R. (2002) A Library of
Adaptive Neural Networks for Control Purposes, IEEE International
Symposium on Computer Aided Control System Design, Glasgow,
Scotland, UK.
[20] Yang, Z. and Blanke, M. (2000). Robust Control Mixer Module Method
for Control Reconguration. Proc. of American Control Conference,
Chicago, Illinois, pp. 3407-3411.
@article{"International Journal of Mechanical, Industrial and Aerospace Sciences:63177", author = "F. Caliskan", title = "Neural Network Based Icing Identification and Fault Tolerant Control of a 340 Aircraft ", abstract = "This paper presents a Neural Network (NN) identification of icing parameters in an A340 aircraft and a reconfiguration technique to keep the A/C performance close to the performance prior to icing. Five aircraft parameters are assumed to be considerably affected by icing. The off-line training for identifying the clear and iced dynamics is based on the Levenberg-Marquard Backpropagation algorithm. The icing parameters are located in the system matrix. The physical locations of the icing are assumed at the right and left wings. The reconfiguration is based on the technique known as the control mixer approach or pseudo inverse technique. This technique generates the new control input vector such that the A/C dynamics is not much affected by icing. In the simulations, the longitudinal and lateral dynamics of an Airbus A340 aircraft model are considered, and the stability derivatives affected by icing are identified. The simulation results show the successful NN identification of the icing parameters and the reconfigured flight dynamics having the similar performance before the icing. In other words, the destabilizing icing affect is compensated. ", keywords = "Aircraft Icing, Stability Derivatives, Neural NetworkIdentification, Reconfiguration.", volume = "1", number = "4", pages = "210-6", }
