针对小型无人机研制过程中飞行控制系统设计评估对气动参数的需求,提出采用结合最大似然准则的自适应遗传算法进行无人机气动参数辨识。该算法引入非线性的适应值进行排名选择、自适应变异策略和局部的杂交,可以减少传统遗传算法过早收敛和停滞的现象。利用某串列翼无人机的程控飞行试验数据验证算法,结果表明算法具有良好的实用性,且对所有布局的固定翼无人机都适用。

Aiming at the requirement of aerodynamic parameters for the design of flight control system in the development of small unmanned aerial vehicles(UAV), an adaptive genetic algorithm combined with the maximum likelihood criterion is proposed to identify the aerodynamic parameters of the UAV. The algorithm introduces nonlinear adaptive values for ranking selection, adaptive mutation strategy and local hybridization, which can reduce the phenomenon of premature convergence and stagnation of traditional genetic algorithms. Using the program-controlled flight test data of a tandem wing UAV to test and verify the algorithm, the results show that the algorithm has good practicability and is applicable to all fixed-wing UAVs.