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

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。

Identification of Spinning Projectile Aerodynamic Parameters Using Adaptive Chaotic Mutation Particle Swarm Optimization

The maximum likelihood estimation is applied to the identification of spinning projectile aerodynamic parameters. A new algorithm called adaptive chaotic mutation particle swarm optimization is proposed to solve the optimal solution of aerodynamic parameters, thus obtaining the aerodynamic parameters of a spinning projectile. The proposed algorithm is to use an adaptive weight function, generate the initial particles based on chaos theory, and set a discriminant mechanism which judges whether the algorithm falls into the local optimum. If the algorithm falls into the local convergence, the mutation operator is used to make the algorithm jump out of local. The common test function is used to test this algorithm. The test result shows that the proposed algorithm has the advantages of more quick convergence, higher optimization precision and wide range of application compared to basic PSO. Simulated ballistic data is used to test the algorithm. The result shows that the proposed algorithm can identify the aerodynamic parameters effectively with high precision and quickly converging velocity.