动能拦截器(KKV)控制系统的设计不确定性参数较多,弹道仿真复杂。 在建立弹目运动方程的基础上将模拟退火算法(SA)引入并行蚁群算法(ACO),结合ACO算法的快速寻优能力和SA的概率突跳特性,对决定KKV姿态和轨道控制精度的6个复杂参数进行了全局优化,优化后的参数使得KKV发动机总体拦截工作时间缩短。仿真表明,与单一ACO和SA算法相比,ACO-SA混合优化在解决复杂的KKV控制参数设置问题上有较强的寻优能力和较快的收敛速度。

The control system of Kinetic Kill Vehicle (KKV) has many uncertain parameters and the trajectory simulation is complicated. The motion equations of target and KKV were established,and Simulated Annealing(SA) was introduced into parallel Ant Colony Optimization (ACO),thus could combine the rapid optimization ability of parallel ACO with probability jumping property of SA. Six complex parameters that determine the precision of orbit control and attitude of the KKV were optimized,and the optimized parameters shortened the working time of KKV motors availably. Simulation indicated that the performance of ACO-SA hybrid optimization method is better than that of ACO or SA algorithm on finding optimal solution and quick convergence in solving complex parameters design problem of the KKV control system.