H∞ Parameter Identification and H2 Feedback Control Synthesizing for Inflight Aircraft Icing

doi:10.1007/s12204-013-1401-6

Abstract

Abstract: Aircraft icing accident happens frequently. Researchers try to find new ways to solve this problem. The study is facing the direction of intelligent inspection and control system. Previous studies focused on the principle of aircraft icing and its effects on flight performance. The onboard icing detection equipment can only give the qualitative icing information, but cannot effectively describe how serious the consequences would be. If the icing detection equipment fails, it will cause a serious threat to flight safety. This paper reviews the smart icing system and its fundamental principle. Then based on H∞ theory, an aircraft icing parameter identification method is introduced, and its feasibility is verified by simulation results. Moreover, this method can work normally under noise interference and measurement error. Icing parameter identification method can also test part of aircraft’s stability or control derivatives which would be changed obviously after aircraft icing. Classified by neural networks, the stability or control derivatives’ variation can be mapped to ice parameters’ variation that reflects the severity of aircraft icing. Then H2 state feedback control is designed originally to suppress the impact of noise interference, so aircraft can keep steady after it is iced. Seeing from simulation result of the whole system, it is clear that the system can effectively detect icing parameters and by using feedback control system, it can ensure the safety of aircraft in the flight envelope.

Key words: aircraft icing| parameter identification| H-infinity| feedback control

摘要： Aircraft icing accident happens frequently. Researchers try to find new ways to solve this problem. The study is facing the direction of intelligent inspection and control system. Previous studies focused on the principle of aircraft icing and its effects on flight performance. The onboard icing detection equipment can only give the qualitative icing information, but cannot effectively describe how serious the consequences would be. If the icing detection equipment fails, it will cause a serious threat to flight safety. This paper reviews the smart icing system and its fundamental principle. Then based on H∞ theory, an aircraft icing parameter identification method is introduced, and its feasibility is verified by simulation results. Moreover, this method can work normally under noise interference and measurement error. Icing parameter identification method can also test part of aircraft’s stability or control derivatives which would be changed obviously after aircraft icing. Classified by neural networks, the stability or control derivatives’ variation can be mapped to ice parameters’ variation that reflects the severity of aircraft icing. Then H2 state feedback control is designed originally to suppress the impact of noise interference, so aircraft can keep steady after it is iced. Seeing from simulation result of the whole system, it is clear that the system can effectively detect icing parameters and by using feedback control system, it can ensure the safety of aircraft in the flight envelope.

关键词: aircraft icing| parameter identification| H-infinity| feedback control

CLC Number:

V 212.1

YING Si-bin1* (应思斌), GE Tong1 (葛彤), AI Jian-liang2 (艾剑良). H∞ Parameter Identification and H2 Feedback Control Synthesizing for Inflight Aircraft Icing[J]. Journal of shanghai Jiaotong University (Science), 2013, 18(3): 317-325.

References

[1] Cole J A, Sands W R. Statistical study of aircraft icing accidents [C]//Proceedings of 29th AIAA Aerospace Sciences Meeting and Exhibit. Reno, Nevada: AIAA, 1991: AIAA-91-0558.
[2] Bragg M B, Broeren A, Addy H, et al. Airfoil iceaccretion aerodynamics simulation [C]//Proceedings of 45th AIAA Aerospace Sciences Meeting and Exhibit. Reno, Nevada: AIAA, 2007: AIAA 2007-0085.
[3] Bragg M B, Hutchison T, Merret J, et al. Effect of ice accretion on aircraft flight dynamics [C]//Proceedings of 38th AIAA Aerospace Sciences Meeting and Exhibit. Reno, Nevada: AIAA, 2000: AIAA-2000-0360.
[4] Pokhariyal D, Bragg M B, Hutchison T, et al. Aircraft flight dynamics with simulated ice accretion [C]//Proceedings of 39th AIAA Aerospace Sciences Meeting and Exhibit. Reno, Nevada: AIAA, 2001: AIAA-2001-0541.
[5] Bragg M B, Basar T, Perkins W R, et al. Smart icing systems for aircraft icing safety [C]//Proceedings of 40th AIAA Aerospace Sciences Meeting and Exhibit. Reno, Nevada: AIAA, 2002: AIAA 2002-0813.
[6] Merret J, Hossain K N, Bragg M B. Envelope protection and atmospheric disturbances in icing encounters [C]//Proceedings of 40th AIAA Aerospace Sciences Meeting and Exhibit. Reno, Nevada: AIAA, 2002: AIAA 2002-0814
[7] Hossain K N, Sharma V, Bragg M B, et al. Envelope protection and control adaptation in icing encounters [C]//Proceedings of 41th AIAA Aerospace Sciences Meeting and Exhibit. Reno, Nevada: AIAA, 2003: AIAA 2003-0025.
[8] Whalen E, Bragg M B. Aircraft characterization in icing using flight test data [J]. Journal of Aircraft, 2005, 42(3): 792-794.
[9] Melody J W, Basar T, Perkins W R. et, al. Parameter identification for inflight detection of aircraft icing [J]. Control Engineering Practice, 2000, 8: 985-1001.
[10] Melody J W, Hillbrand T, Basar T. et al. Hinfinity parameter identification for in-flight detection of aircraft icing: The time varying case [J]. Control Engineering Practice, 2001, 9: 1327-1335.
[11] Schuchard E A, Melody J W, Basar T. Detection and classification of aircraft icing using neural networks [C]//Proceedings of 39th AIAA Aerospace Sciences Meeting and Exhibit. Reno, Nevada: AIAA, 2001: AIAA-2000-0361.
[12] Ratvasky T P, Van Zante J, Riley J T. NASA/FAA tailplane icing program overview [C]//Proceedings of 37th AIAA Aerospace Sciences Meeting and Exhibit. Reno, Nevada: AIAA, 1999: AIAA-99-0370.
[13] Roskam J. Airplane flight dynamics and automatic flight controls[M]. Ottawa, KS: Roskam Aviation and Engineering Corporation, 1998.
[14] Didinsky G, Pan Z, Basar T. Parameter identification of uncertain plants using H∞ methods [J]. Automatica, 1995, 31(9): 1227-1250.
[15] Ratvasky T P, Ranaudo R J. Icing elects on aircraft stability and control determined from fight data [C]//Proceedings of 31th AIAA Aerospace Sciences Meeting and Exhibit. Reno, Nevada: AIAA, 1993: AIAA-93-0398.