Frequency domain global optimization algorithm for the aircraft flutter model parameter identification

For stochastic models with input and output measurement noises in aircraft flutter experiment, the maximum likelihood cost function´s simple form is firstly proposed by means of frequency domain maximum likelihood estimation principle. Then a global optimization iterative convolution smoothing identification method is derived to significantly reduce the possibility of convergence to a local minimum and weakly dependent of the starting values´ choice by using the global optimization theory. The identification method modifies the iterative method with a stochastic perturbation term and guarantees the algorithm converge to a global minimum. The simulation with real flight test data shows the efficiency of the algorithm.