基于自适应混沌变异粒子群优化算法的旋转弹丸气动参数辨识
发布时间：2018-11-06 15:17
【摘要】：将最大似然准则应用于高速旋转弹丸的气动参数辨识问题中,提出一种新的自适应混沌变异粒子群算法求解该准则下的气动参数最优解,进而得到弹丸的气动参数。该算法通过自适应调整惯性权重、利用混沌优化的思想产生初始粒子、设定早熟判别机制来判断是否陷入局部最优解,并通过粒子变异的策略使其跳出局部最优解等方法进一步优化基本粒子群算法。通过常用的测试函数对该算法进行了测试,测试结果表明:相比于基本粒子群算法,该算法具有收敛速度快、寻优精度高、应用范围广等优点。利用系统仿真的方法模拟弹丸的自由飞行数据,并利用该数据结合所提算法对弹丸的主要气动参数进行辨识,辨识结果表明:该算法可以有效辨识弹丸的气动参数,且精度高,收敛速度快,可以应用于工程实际问题。
[Abstract]:The maximum likelihood criterion is applied to the aerodynamic parameter identification of high speed rotating projectile. A new adaptive chaotic mutation particle swarm optimization algorithm is proposed to solve the optimal solution of aerodynamic parameters under the criterion, and then the aerodynamic parameters of the projectile are obtained. The algorithm adaptively adjusts inertial weight, generates initial particles by using the idea of chaos optimization, and sets up a precocious discriminant mechanism to determine whether or not it falls into a local optimal solution. The basic particle swarm optimization algorithm is further optimized by the strategy of particle mutation which makes it jump out of the local optimal solution and so on. The test results show that compared with the basic particle swarm optimization algorithm, the algorithm has the advantages of fast convergence, high precision and wide application. The system simulation method is used to simulate the free-flight data of the projectile, and the main aerodynamic parameters of the projectile are identified by using the data and the proposed algorithm. The identification results show that the algorithm can effectively identify the aerodynamic parameters of the projectile, and the accuracy of the algorithm is high. The convergence rate is fast and can be applied to practical engineering problems.
【作者单位】： 南京理工大学瞬态物理国家重点实验室;海军驻沈阳弹药专业军事总代表室;南京理工大学能源与动力工程学院;
【基金】：国家自然科学基金项目(11472136;11402117)
【分类号】：TJ410;TP18
本文编号：2314662
[Abstract]:The maximum likelihood criterion is applied to the aerodynamic parameter identification of high speed rotating projectile. A new adaptive chaotic mutation particle swarm optimization algorithm is proposed to solve the optimal solution of aerodynamic parameters under the criterion, and then the aerodynamic parameters of the projectile are obtained. The algorithm adaptively adjusts inertial weight, generates initial particles by using the idea of chaos optimization, and sets up a precocious discriminant mechanism to determine whether or not it falls into a local optimal solution. The basic particle swarm optimization algorithm is further optimized by the strategy of particle mutation which makes it jump out of the local optimal solution and so on. The test results show that compared with the basic particle swarm optimization algorithm, the algorithm has the advantages of fast convergence, high precision and wide application. The system simulation method is used to simulate the free-flight data of the projectile, and the main aerodynamic parameters of the projectile are identified by using the data and the proposed algorithm. The identification results show that the algorithm can effectively identify the aerodynamic parameters of the projectile, and the accuracy of the algorithm is high. The convergence rate is fast and can be applied to practical engineering problems.
【作者单位】： 南京理工大学瞬态物理国家重点实验室;海军驻沈阳弹药专业军事总代表室;南京理工大学能源与动力工程学院;
【基金】：国家自然科学基金项目(11472136;11402117)
【分类号】：TJ410;TP18
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1 王贵东;崔尔杰;刘子强;;闭环气动参数辨识的两步方法[J];飞行力学;2010年02期
，本文编号：2314662
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