The aircraft flutter model parametric identification based on frequency domain global optimization algorithm

Abstract

With regard to the aircraft flutter flight test stochastic models coexisting input and output observation noise, this paper deduces the simplified form of the maximum likelihood cost function about the stochastic model by virtue of the 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.