期刊文献+

高超声速飞行器再入过程改进气动系数模型 被引量:22

Modified aerodynamic coefficient models of hypersonic vehicle in reentry phase
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摘要 针对高超声速飞行器再入过程气动系数模型和参数辨识问题,基于公开的气动系数数据,综合考虑攻角和马赫数两个主要因素,分析了气动系数与二者的函数关系,建立了高超声速飞行器的改进升力系数和阻力系数模型,采用非线性最小二乘法进行模型参数辨识,得到参数辨识结果。将已知的气动数据与改进气动系数模型计算值进行对比,升力系数和阻力系数的相对误差平均值均小于5.10%,表明所建立的改进气动系数模型具有较高的精度,可以用于高超声速飞行器再入轨迹优化和仿真。 The aerodynamic coefficients model of hypersonic vehicle in reentry phase and parameters identification are considered.The relationships between the aerodynamic coefficients and main factors including angle of attack and Mach number are analyzed based on the public aerodynamic coefficients database.The modified lift and drag coefficients models are built and the parameters of the models are identified by the nonlinear least square method.The comparisons between the database and the value by the modified aerodynamic coefficients models show that the averages of the relative errors are all less than 5.10%,which demonstrates that the modified aerodynamic coefficients models have the high precision and can be used in reentry trajectory optimization and simulation.
出处 《系统工程与电子技术》 EI CSCD 北大核心 2011年第1期134-137,共4页 Systems Engineering and Electronics
基金 国家自然科学基金(60710002)资助课题
关键词 参数辨识 气动系数模型 非线性最小二乘 高超声速飞行器 parameter identification aerodynamic coefficient model nonlinear least square hypersonic vehicle
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参考文献15

  • 1Voland R T, Huebner I. D. X-43A hypersonic vehicle technology development[J]. Acta Astronautica, 2006,59 (3) : 181 - 191.
  • 2Peebles C. Learning from experience; case studies of the hyper-X project[C]// 47th AIAA Aerospace Sciences Meeting, 2009 : 1 - 6.
  • 3Davis M C, White J T. X- 43A flight-test determined aerodynamic force and moment characteristics at mach 7.0[J]. Journal of Spacecraft and Rockets, 2008,45 (3) : 472 - 484.
  • 4Brunner C W, Lu P. Skip entry trajectory planning and guidance[J]. Journal of Guidance, Control, and Dynamics, 2008, 31 (5): 1210 -1219.
  • 5Desai P N, Conway B A. Six-degree-of freedom trajectory optimization using a two-timescale collocation architecture [J].Journal of Guidance, Control, and Dynamics, 2008, 31 ( 5 ) : 1308 - 1315.
  • 6Jain S, Tsiotras P. Trajectory optimization using multiresolution techniques[J]. Journal of Guidance, Control, and Dynamics, 2008,31(5) :1424 - 1436.
  • 7Jorris T R, Cobb R G. Three-dimensional trajectory optimization satisfying waypoint and no-fly zone constraints[J]. Journal of Guidance, Control, and Dynamics, 2009,32(2) :551- 572.
  • 8Franulovic M, Basan R. Genetic algorithm in material model parameters' identification for low-cycle fatigue[J]. Computa tional Materials Science, 2009,45(2) :505 - 510.
  • 9谷川,潘国荣,施贵刚,陈兴权.基于遗传算法的曲面拟合参数辨识[J].武汉大学学报(信息科学版),2009,34(8):983-986. 被引量:7
  • 10刘琳,陈仁文,刘强,王鑫伟.基于灰色累加和加权乘积的模型辨识算法[J].系统工程与电子技术,2010,32(5):976-979. 被引量:3

二级参考文献21

  • 1黄绪明.一类改进的遗传算法[J].长沙大学学报,2005,19(5):1-4. 被引量:10
  • 2王福林,王吉权,吴昌友,吴秋峰.实数遗传算法的改进研究[J].生物数学学报,2006,21(1):153-158. 被引量:30
  • 3王解先.工业测量中一种二次曲面的拟合方法[J].武汉大学学报(信息科学版),2007,32(1):47-50. 被引量:48
  • 4刘成龙,杨天宇.基于BP神经网络的GPS高程拟合方法的探讨[J].西南交通大学学报,2007,42(2):148-152. 被引量:30
  • 5方胜彦.现代控制理论[M].北京:科学出版社,1984.164-170.
  • 6Sira-Ramírez H,Fliess M.On discrete-time uncertain visual based control of planar manipulators:an online algebraic identification approach[C] ∥ Proc.of 41st IEEE Conference on Decision and Control,2002:4509-4514.
  • 7Stefan F.Algebraic linear identification,modelling,and application of flatness-based control[D].Linz:Johannes Kepler University,2006.
  • 8邓聚龙.灰色理论基础[M].武汉:华中科技大学出版社,2002..
  • 9[美]GAF塞伯.线性回归分析[M].北京:科学出版社,1987.
  • 10J J E Slotine, W Li. Applied nonlinear control[ M]. Englewood Cliffs,USA : Prentice - Hall,1991.

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