Fluid Mechanics and Flight Mechanics

Identification of aircraft stability and control characteristics derivatives and analysis of random noises

  • DING Di ,
  • QIAN Weiqi ,
  • WANG Qing
Expand
  • 1. State Key Laboratory of Aerodynamics, China Aerodynamics Research and Development Center, Mianyang 621000, China;
    2. Computational Aerodynamics Institute, China Aerodynamics Research and Development Center, Mianyang 621000, China

Received date: 2014-08-27

  Revised date: 2014-12-01

  Online published: 2014-12-11

Abstract

Small perturbation theory and parameter identification method are applied to obtaining the stability and control derivatives of an airplane. To accomplish the stability and control characteristics analysis during the airplane design, we develop the parameter identification algorithm, which are validated with data of an unmanned airplane vehicle ANCE and a Boeing transport airplane Boeing 747. Firstly, we estimate the stability and control derivatives of the airplanes and compare them with the small perturbation theory results. Then the accuracy of the estimated derivatives and the dispersion of the eigenvalues of different response modes are quantitatively analyzed by Monte Carlo simulation based on the known measurement noises during the flight test. The estimating accuracy of certain relatively small derivatives, which are dominated by airplanes' inherent aerodynamic characteristics, degenerates with these noises. The response eigenvalues of short-period mode, Dutch-roll mode and roll mode under random noises can be accurately acquired with the parameter identification algorithm presented here, while the response eigenvalues of phugoid mode and spiral mode are more sensitive to noises.

Cite this article

DING Di , QIAN Weiqi , WANG Qing . Identification of aircraft stability and control characteristics derivatives and analysis of random noises[J]. ACTA AERONAUTICAET ASTRONAUTICA SINICA, 2015 , 36(7) : 2177 -2185 . DOI: 10.7527/S1000-6893.2014.0332

References

[1] Roskam J. Evolution of airplane stability and control: a designer's viewpoint[J]. J. GUIDANCE, 1991, 14(3): 481-491.
[2] Yu J C. Flight data processing and aerodynamic parameter identification[D]. Xi'an: Northwestern Polytechnical University, 2007 (in Chinese). 于君彩. 飞行数据处理与气动参数辨识[D]. 西安: 西北工业大学, 2007.
[3] Cardenas E M, Boschetti P J, Amerio A, et al. Design of an unmanned aerial vehicle for ecological conservation, AIAA-2005-7056[R]. Reston: AIAA, 2005.
[4] Boschetti P J, Cardenas E M, Amerio A. Stability of an unmanned airplane using a low-order panel method, AIAA-2010-8121[R]. Reston: AIAA, 2010.
[5] Biber K. Stability and control characteristics of a new FAR23 airplane, AIAA-2006-0255[R]. Reston: AIAA, 2006.
[6] Mehra R K, Stepner D E, Tyler J S. Maximum likelihood identification of aircraft stability and control derivatives[J]. Journal of Aircraft, 1974, 11(2): 81-89.
[7] Speyer J L, Crues E Z. On-line aircraft state and stability derivative estimation using the modified-gain extended Kalman filter[J]. J. GUIDANCE, 1987, 10(3): 262-268.
[8] Tang S J. Three-lifting-surface aircraft disturbance motion linearized model[J]. Flight Dynamics, 2005, 23(1): 27-30 (in Chinese). 唐胜景. 三升力面飞机扰动运动线性化模型[J]. 飞行力学, 2005, 23(1): 27-30.
[9] Yang F, Xiong X, Chen Z J, et al. Modeling system identification and validation of small rotorcraft-based unmanned aerial vehicle[J]. Journal of Beijing University of Aeronautics and Astronautics, 2010, 36(8): 913-917 (in Chinese). 杨帆, 熊笑, 陈宗基, 等. 超小型直升机动力学模型的建模、辨识与验证[J]. 北京航空航天大学学报, 2010, 36(8): 913-917.
[10] Houston S S. Identification of autogyro longitudinal stability and control characteristics[J]. Journal of Guidance, Control, and Dynamics, 1998, 21(3): 391-399.
[11] Rauch H E, Tung F, Striebel C T. Maximum likelihood estimates of linear dynamic systems[J]. AIAA Journal, 1965, 3(8): 1445-1450.
[12] Oshman Y, Mendelboim T. Maximum likelihood identification and realization of stochastic systems[J]. Journal of Guidance, Control, and Dynamics, 1994, 17(4): 692-700.
[13] West N, Swiler L. Parameter estimation via Gaussian processes and maximum likelihood estimation, AIAA-2010-2851[R]. Reston: AIAA, 2010.
[14] Duong V, Stubberud A R. System identification by genetic algorithm[C]//Aerospace Conference Proceedings. Piscataway, NJ: IEEE Press, 2002: 2331-2337.
[15] Qian W Q, Wang Q, Wang W Z, et al. Application of genetic algorithms for aerodynamic parameter estimation[J]. Acta Aerodynamica Sinica, 2003, 21(2): 196-201 (in Chinese). 钱炜祺, 汪清, 王文正, 等. 遗传算法在气动力参数辨识中的应用[J]. 空气动力学学报, 2003, 21(2): 196-201.
[16] Zhang T J, Wang Q, He K F. Application of particle swarm optimization for aerodynamic parameter estimation[J]. Acta Aerodynamica Sinica, 2010, 28(6): 633-638 (in Chinese). 张天姣, 汪清, 何开锋. 粒子群算法在气动力参数辨识中的应用[J]. 空气动力学学报, 2010, 28(6): 633-638.
[17] Roundbari A, Saghafi F. Intelligent modeling and identification of aircraft nonlinear flight[J]. Chinese Journal of Aeronautics, 2014, 27(4): 759-771.
[18] Marwaha M, Valasek J, Singla P. GLOMAP approach for nonlinear system identification of aircraft dynamics using flight data, AIAA-2008-6895[R]. Reston: AIAA, 2008.
[19] Viana F, Maciel B, Neto N, et al. Aircraft longitudinal stability and control derivatives identification by using life cycle and Levenberg-Marquardt optimization algorithms[J]. Inverse Problems in Science and Engineering, 2009, 17(1): 17-34.
[20] Etkin B, Reid L. Dynamics of flight: stability and control[M]. New York: John Wiley & Sons, 1995: 129-258.
[21] Klein V, Morelli E A. Aircraft system identification theory and practice[M]. Reston: AIAA, 2006: 181-200.

Outlines

/