航空学报 > 2011, Vol. 32 Issue (2): 202-211   doi: CNKI:11-1929/V.20101118.0908.001

直升机悬停状态全耦合飞行动力学模型辨识方法

吴伟, 陈仁良   

  1. 南京航空航天大学 直升机旋翼动力学重点实验室, 江苏 南京 210016
  • 收稿日期:2010-04-22 修回日期:2010-07-19 出版日期:2011-02-25 发布日期:2011-02-25

Identification Method for Helicopter Fully Coupled Flight Dynamics Model in Hover Condition

WU Wei, CHEN Renliang   

  1. Science and Technology on Rotorcraft Aeromechanics Laboratory, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, China
  • Received:2010-04-22 Revised:2010-07-19 Online:2011-02-25 Published:2011-02-25

摘要: 提出了一种基于集员辨识理论的直升机全耦合飞行动力学模型参数辨识方法。该方法针对直升机飞行动力学模型耦合强,难以得到辨识模型和待辨识参数之间显式函数关系的特点,推导并建立了状态空间微分方程形式模型集员辨识的间接辨识算法。通过引入广义噪声的概念以及对其边界的灵活设定,实现了对辨识参数众多、耦合严重且灵敏度差异大的复杂模型的集员辨识。在此基础上,建立了直升机全耦合飞行动力学模型集员辨识的两步法。根据BO-105直升机的飞行试验数据,利用该方法辨识得到的全耦合飞行动力学模型与飞行试验数据相比具有良好的一致性;与基于统计理论的传统辨识方法相比,该方法提高了辨识精度,加快了辨识速度且具有较高的鲁棒性。

关键词: 参数辨识, 集员辨识, 直升机, 飞行动力学, 状态空间

Abstract: A method based on the set-membership identification theory for the parameter identification of a helicopter fully coupled flight dynamics model is presented. In view of the fact that the helicopter flight dynamics model is seriously coupled and that it is difficult to obtain explicit function relationships between the identification model and the parameters to be identified, an indirect identification algorithm is created in this method for the set-membership identification in the form of a state-space differential equation. The concept of generalized noise is introduced to implement the set-membership identification of a complex model which has numerous identification parameters that are seriously coupled and greatly differentiated in sensitivity. Then a two-step method for the set-membership identification of a helicopter fully coupled flight dynamics model is established. A comparison with the flight test data of helicopter BO-105 demonstrates that the identification model using this method has good coherence with the data. Compared with the traditional method which is based on statistic theory, the new method can enhance identification accuracy, speed and robustness.

Key words: parameter identification, set-membership identification, helicopter, flight dynamics, state-space

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