The application of separable least square algorithms based on global nonlinear for the parametric identifications of airplane flutter model

In this paper, we extend the biased compensated least-squares method (CLS) to get the nonlinear separable least squares (NSLS) when the observed input-output data are corrupted with noise. The nonlinear separable least square algorithm is adopted for aircraft flutter modal parameter identification under noisy environment. Combing with a rational transfer function model, the identification of system with noisy data is transformed into a nonlinear separable least square problem. Using this algorithm, the noise variance parameters and the model parameters can be obtained separately. The simulation with real flight test data shows the efficiency of the algorithm.