【摘要】 针对传统牛顿迭代法在辨识弹丸气动参数时需要精确估计参数初值的问题,提出了基于粒子群初值选取的牛顿迭代优化算法辨识弹丸的零升阻力系数。采用弹丸的六自由度模型作为系统模型,以最大似然准则作为辨识判据,结合粒子群算法的群体搜索性以及牛顿迭代法的局部细致搜索性,对辨识判据进行了优化,并且根据灵敏度计算分析了参数的可辨识性。通过仿真和实际数据辨识对算法的精确性和可靠性进行了验证。仿真和实际辨识结果表明,该方法能有效地辨识旋转弹丸零升阻力系数,可为进一步提高射表精度、节省用弹量提供参考价值。更多还原

【Abstract】 Traditional aerodynamic parameter identification using Newton algorithm has strict requirement for initial parameter. Combining the advantages of particle swarm algorithm in initial value selection and the advantages of Newton iteration method in precise iteration,this paper presented a new Newton iteration algorithm using the initial value from particle swarm algorithm to identify the spinning projectile’s drag coefficient. Then,using the 6 degree model and maximum likelihood rule,we analyzed the sensitivity.Finally processing the actual speed data of spinning projectile,we acquired a satisfied result. According to the analysis of the result,it’s concluded that the algorithm can effectively get a satisfied result in the process of parameter identification,which is of great value in compiling fire table and saving the bomb.更多还原