基于遗传算法的大气层外拦截弹拦截优化(英文)

INTERCEPT OPTIMIZATION OF EXO-ATMOSPHERIC INTERCEPTOR BASED ON GENETIC ALGORITHM

提出了一种同时考虑大气层外拦截弹中段和末段飞行过程的拦截优化方法。首先建立了大气层外拦截弹中段和末段飞行的动力学模型,并设计了速度增益中制导律和鲁棒变结构末制导律;然后确定了影响拦截性能的优化控制参数及其约束条件,以拦截过程的燃料消耗质量和脱靶量最小为组合性能指标;最后采用具有全局最优性的遗传算法对这一拦截优化问题进行了仿真研究,并与复形调优算法的结果进行了比较。仿真结果表明,遗传算法与传统优化方法相比在解决大气层外拦截弹带约束复杂非线性组合优化问题时,能更好地收敛到全局最优值,并能有效降低大气层外拦截的燃料消耗和脱靶量。

An intercept optimization approach of the exo-atmospheric interceptor is proposed by the middle and terminal flight stages. Firstly, the dynamic models of the exo-atmospheric interceptor in middle and terminal flight stages are constructed ; and the velocity gain midcourse guidance law and the robust variable structure terminal guidance law are designed. Then the optimization parameters and their constraints affecting the intercept performance are determined. The genetic algorithm （GA） with the advantage of global optimization is used to deal with the intercept optimization problem. The performance index of the optimization is composed of the minimum fuel consumption and the minimum miss distance of the interception. Finally, optimization results of GA and the complex algorithm （CA） are compared. Simulation results show that compared with the traditional opti- mization method, GA can converge to the global optimization better in solving the complex constrained nonlinear combinatorial optimization of the exo-atmospheric interceptor, and reduce the fuel consumption and the miss distance.