Robust flutter analysis based on genetic algorithm

Robust flutter analysis considering model uncertain parameters is very important in theory and engineering applications. Modern robust flutter solution based on structured singular value subject to real parametric uncertainties may become difficult because the discontinuity and increasing complexity in real mu analysis. It is crucial to solve the worst-case flutter speed accurately and efficiently for real parametric uncertainties. In this paper, robust flutter analysis is formulated as a nonlinear programming problem. With proper nonlinear programming technique and classical flutter analysis method, the worst-case parametric perturbations and the robust flutter solution will be captured by optimization approach. In the derived nonlinear programming problem, the parametric uncertainties are taken as design variables bounded with perturbed intervals, while the flutter speed is selected as the objective function. This model is optimized by the genetic algorithm with promising global optimum performance. The present approach avoids calculating purely real mu and makes robust flutter analysis a plain job. It is illustrated by a special test case that the robust flutter results coincide well with the exhaustive method. It is also demonstrated that the present method can solve the match-point robust flutter solution under constant Mach number accurately and efficiently. This method is implemented in problem with more uncertain parameters and asymmetric perturbation interval.