基于改进SA-PSO算法的飞机气动参数辨识算法OA

A novel maximum likelihood aerodynamic parameter identification algorithm based on improved Simulated Annealing-Particle Swarm Optimization(SA-PSO)is proposed to address the issue of aircraft dynamics analysis under uncertain aerodynamic parameters.This algorithm overcomes the sensitivity of the traditional Newton-Raphson algorithm to initial values when solving maximum likelihood problems.Moreover,a dynamic weight strategy is employed to enhance the global optimization performance of the PSO algorithm.The Metropolis Criterion from the SA algorithm is incorporated to improve the individual and global optimal update strategies of the PSO algorithm,effectively mitigating the premature convergence commonly observed in traditional PSO algorithms.Simulation results demonstrate that the proposed algorithm exhibits superior global search capabilities and achieves higher accuracy in parameter identification compared to both the standard PSO and Newton-Raphson algorithms.