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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.