H∞ parameter identification for inflight icing detection is discussed in this paper for the development of the de-icing and anti-icing system of a large icing research prototype. First, the H∞ method parameters are tuned and chosen.Then the method is evaluated by the airplane's longitudinal simulation data with measurement noises. The identification results show that the method can track the time-varying aerodynamic parameters in the ice accretion process, and that the maximum normalized Root Mean Square(RMS) error 11% indicates high accuracy of identification.The 81 different ice accretion processes are then identified by the H∞ method.The results show that when the ice accretion process changes slowly and lasts long the identification accuracy is relatively poor, and that when the ice accretion time is between 100-300 s the accuracy is relatively high. The accuracy of H∞ algorithm in the different standard deviations of measurement noises is analyzed by Monte Carlo simulation.The error and delay statistic characteristics of the three longitudinal aerodynamic derivatives show that the identification accuracy of derivatives of lift and pitching moment to angle of attack is relatively high as their mean normalized RMS errors are 1.8% and 4% respectively, and their mean delays are 3 s and 9.5 s respectively.
DING Di
,
CHE Jing
,
QIAN Weiqi
,
WANG Qing
. Aerodynamic parameter identification for aircraft wing icing using H∞ method[J]. ACTA AERONAUTICAET ASTRONAUTICA SINICA, 2018
, 39(3)
: 121626
-121626
.
DOI: 10.7527/S1000-6893.2017.21626
[1] GORAJ Z.An overview of the deicing and anti-icing technologies with prospects for the future[C]//24th International Congress of Aeronautical Sciences, 2004.
[2] JARVINEN P. Aircraft ice detection method:AIAA-2007-696[R].Reston, VA:AIAA, 2007.
[3] CALISKAN F, HAJIYEV C. A review of inflight detection and identification of aircraft icing and reconfigurable control[J]. Progress in Aerospace Sciences, 2013, 60:12-34.
[4] BRAGG M B, BASAR T, PERKINS W R, et al. Smart icing systems for aircraft icing safety:AIAA-2002-0813[R]. Reston, VA:AIAA, 2002.
[5] WENZ A, JOHANSEN T A. Icing detection for small fixed wing UAVs using inflight aerodynamic coefficient estimation[C]//IEEE Conference on Control Applications. Piscataway, NJ:IEEE Press, 2016.
[6] MELODY J W, BASAR T, PERKINS W R, et al. Parameter identification for inflight detection and characterization of aircraft icing[J]. Control Engineering Practice, 2000, 8(9):985-1001.
[7] CRISTOFARO A, JOHANSEN T A, AGUIAR A P. Icing detection and identification for unmanned aerial vehicles:Multiple model adaptive estimation[C]//European Control Conference, 2015.
[8] 占荣辉,张军. 非线性滤波理论与目标跟踪应用[M]. 北京:国防工业出版社,2013:8-11. ZHAN R H, ZHANG J. Nonlinear filtering theory with target tracking application[M]. Beijing:National Defense Industry Press, 2013:8-11(in Chinese).
[9] SIMON D. 最优状态估计:卡尔曼, H∞及非线性滤波[M].张勇刚, 李宁, 奔粤阳, 译. 北京:国防工业出版社,2015:255-269. SIMON D. Optimal state estimation:Kalman, H∞ and nonlinear approaches[M]. ZHANG Y G, LI N, BEN Y Y, translated. Beijing:National Defense Industry Press, 2015:255-269(in Chinese).
[10] DIDINSKY G, PAN Z, BASAR T. Parameter identification for uncertain plants using H∞methods[J]. Automatica, 1995, 31(9):1227-1250.
[11] MELODY J W, HILLBRAND T, BASAR T, et al. H∞ parameter identification for inflight detection of aircraft icing:The time-varying case[J]. Control Engineering Practice, 2001, 9(12):1327-1335.
[12] MELODY J W. Inflight characterization of aircraft icing[D]. Illinois Urbana:University of Illinois at Urbana-Champaign Graduate College, 2004:1-6.
[13] SCHUCHARD E A, MELODY J W, BASAR T, et al. Detection and classification of aircraft icing using Neural Networks:AIAA-2000-0361[R]. Reston, VA:AIAA, 2000.
[14] DONG Y Q, AI J L. Research on inflight parameter identification and icing location detection of the aircraft[J]. Aerospace Science and Technology, 2013, 29(1):305-312.
[15] DONG Y Q, AI J L. Inflight parameter identification and icing location detection of the aircraft:The time-varying case[J/OL]. Journal of Control Science and Engineering, 2014:1-11.[2014-07-10].http://dx.doi.org/10.1155/2014/396532.
[16] 应思斌, 葛彤, 艾剑良. 飞机结冰时不变参数辨识技术[J]. 指挥控制与仿真, 2012, 34(4):55-60. YING S B, GE T, AI J L. Time invariant parameter identification of inflight aircraft icing[J]. Command Control & Simulation, 2012, 34(4):55-60(in Chinese).
[17] 应思斌, 葛彤, 艾剑良. 飞机结冰气动参数综合检测方法研究[J]. 指挥控制与仿真, 2012, 34(5):128-133. YING S B, GE T, AI J L. Research on comprehensive parameter identification of inflight aircraft icing[J]. Command Control & Simulation, 2012, 34(5):128-133(in Chinese).
[18] YING S B, GE T, AI J L. H∞ parameter identification and H2 feedback control synthesizing for inflight aircraft icing[J]. Journal of Shanghai Jiaotong University, 2013, 18(3):317-325.
[19] RATVASKY T P, RANAUDO R J. Icing effects on aircraft stability and control determined from flight data:AIAA-1993-0398[R]. Reston, VA:AIAA, 1993.
[20] BRAGGM B, HUTCHISON T, MERRET J, et al. Effect of ice accretion on aircraft flight dynamics:AIAA-2000-0360[R]. Reston, VA:AIAA, 2000.
[21] AYKAN R, HAJIYEV C, CALISKAN F. Aircraft icing detection, identification and reconfigurable control based on Kalman filtering and neural networks:AIAA-2005-6220[R]. Reston, VA:AIAA, 2005.