An Improved PSO-SA-ML Algorithm for Longitudinal Aerodynamic Parameter Identification of the Aircraft

Aerodynamic parameter identification is one of the effective methods to obtain aerodynamic parameters of aircraft. Aiming at the problems that the maximum likelihood (ML) method is susceptible to the initial value, and the basic particle swarm optimization (PSO) is easy to precocious convergence. An improved PSO-SA-ML algorithm is proposed to identify aerodynamic parameters of the aircraft in this paper. The PSO algorithm is integrated with the maximum likelihood algorithm, and the adaptive inertia weight is added to improve it. Mean-while, the Metropolis criterion of the simulated annealing (SA) algorithm is introduced. The simulation results prove that the proposed improved PSO-SA - ML algorithm has the possibility of accepting poor solutions, enhances the optimization ability, and improves the convergence speed compared with basic PSO algorithm.