粒子群算法在气动力参数辨识中的应用  

Application of particle swarm optimization for aerodynamic parameter estimation

作　　者：张天姣[1,2] 汪清[1,2] 何开锋[1,2] 

机构地区：[1]空气动力学国家重点实验室,四川绵阳621000 [2]中国空气动力研究与发展中心计算空气动力学研究所,四川绵阳621000

出　　处：《空气动力学学报》2010年第6期633-638,共6页Acta Aerodynamica Sinica

基　　金：空气动力学国家重点实验室基金(SKLA2009A0103)

摘　　要：将粒子群优化算法推广应用于气动力参数辨识,以取代传统的梯度类优化算法。通过采用加入动态改变惯性权重的粒子群算法对A型、B型战术导弹的纵向和横向气动力参数进行辨识计算及分析后,可以看到:(1)粒子群算法是气动力辨识的一种新的有效方法,该算法不受参数初值选取的影响,具有较好的全局寻优特性;(2)粒子群算法的计算效率受算法构造本身等因素的影响比较大、对粒子群规模不十分敏感,并且还有相当大的进一步完善与改进的空间。Maximum Likelihood Estimation（MLE） method is the most prevailing method in aerodynamic parameter estimation.The most difficult problem of MLE is that the conventional gradient-based optimization method in MLE relies too much on the selection of initial values of optimization.So,Particle Swarm Optimization（PSO） adopting the dynamic inertia weight is applied in aerodynamic parameter estimation to replace those gradient-based optimization methods.After implementing the PSO to estimate the longitudinal and lateral aerodynamic parameters of two different tactical missiles,some preliminary conclusions can be drawn.First,PSO is a new effective method to estimate aerodynamic parameters,and it is not restricted by the selection of initial values of parameters and able to find the globally optimal point effectively.Second,the efficiency of PSO is also influenced by other factors such as the selection of weight,etc.,the efficiency of basic PSO is greatly improved after adopting the dynamic inertia weight thanks to the ability of the dynamic inertia weight to accelerate the rapidity of convergence and not easy to trap in the local extreme points.Moreover,the efficiency of PSO is not sensitive to the number of population.There is a significant space for the further improvement and refinement of PSO.

关 键 词：粒子群优化算法 气动力参数辨识 战术导弹 

分 类 号：V211.3[航空宇航科学与技术—航空宇航推进理论与工程] TP18[自动化与计算机技术—控制理论与控制工程]