基于遗忘因子算法的飞行器颤振模态参数辨识

采用频率响应函数数据的频域多输入多输出状态空间模型,研究基于遗忘因子的输入输出数据矩阵构造机制,提高辨识算法的收敛速度;针对系统矩阵的求解问题,采用主成分分析法实现对模型参数矩阵的一致性估计,避免了奇异值分解带来的估计有偏性;算法前采用新息方差准则估计出系统的阶数,以减少计算复杂度.最后利用试飞试验数据辨识飞行器的系统参数,验证了该方法的有效性.

Forgetting Factor Algorithm for Aircraft Flutter Modal Parameter Identification

A novel frequency-domain subspace system identification based on forgetting factor was discussed. The key idea was applied for frequency-domain MIMO state-space models starting from frequency response function data and generalized by taking into account the initial and final conditions. It was necessary to guarantee leakage-free effect calculated through the discrete Fourier transform. A forgetting factor was introduced in the Hankel matrices of the input-output data to increase the convergent rate. In order to solve the system matrices problem, principle component analysis was used to determine the system observability subspace matrices and achieve consistent estimates of the system matrices. Before applying the algorithm, the innovation variance criterion was used to estimate the system′s order in order to decrease the complexity. The efficiency of this method was illustrated with a simulation example.