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。

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.