基于组合优化算法的空面导弹全空域弹道优化

祝雯生; 吕先敬; 侯振乾; 张顺家; 毕鹏; 仲康

doi:10.13700/j.bh.1001-5965.2021.0252

基于组合优化算法的空面导弹全空域弹道优化

doi: 10.13700/j.bh.1001-5965.2021.0252

1.
上海机电工程研究所，上海 201109
2.
空军装备部驻上海地区军事代表局，上海 200030

详细信息

通讯作者:
E-mail： whzhu@nuaa.edu.cn

中图分类号: V448.13
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- 被引次数: 0
出版历程
- 收稿日期: 2021-05-14
- 录用日期: 2021-06-29
- 网络出版日期: 2021-07-06
- 整期出版日期: 2023-02-28

Trajectory optimization of air-to-surface missile in full airspace based on combinational optimization algorithm

1.
Shanghai Electro-Mechanical Engineering Institute，Shanghai 201109，China
2.
The Military Representative Bureau of Equipment Department in Shanghai，Shanghai 200030，China

More Information

Corresponding author: E-mail： whzhu@nuaa.edu.cn

摘要

摘要:
针对空面导弹全空域弹道优化问题，提出基于多岛遗传算法和序列二次规划法的组合优化算法，充分发挥多岛遗传算法对初值不敏感、全局收敛性强及序列二次规划法收敛速度快、精度高等特点。采用代理模型技术，在保证拟合精度的前提下，极大的降低了全空域弹道优化的计算量。算例结果表明：组合优化算法收敛速度较快，可以得到高精度的全局最优解；代理模型拟合精度较高，可以满足工程要求；且优化结果和所建立的代理模型可以为全空域制导律设计提供有力的支撑。
- 空面导弹 /
- 全空域 /
- 弹道优化 /
- 多岛遗传算法 /
- 序列二次规划法
Abstract:
To deal with the problem of trajectory optimization for the air-to-surface missile in full airspace, a combinational optimization algorithm based on a multi-island genetic algorithm (MIGA) and sequential quadratic programming (SQP) was proposed. The advantages of being insensitive to initial values and global convergence of MIGA, and rapid convergence and high precision of SQP were developed. With the surrogate model, the computational cost of trajectory optimization in full airspace was greatly reduced on the premise of ensuring fitting precision. The surrogate model significantly lowered the computing cost of trajectory optimization in full airspace under the assumption of fitting precision. Results show that the combinational optimization algorithm had a rapid convergence speed and could obtain a global high-precision solution; the surrogate model had high fitting precision and could satisfy the requirements of engineering; the optimization results and surrogate model could provide an effective way for the design of guidance law in full airspace.
- air-to-surface missile /
- full airspace /
- trajectory optimization /
- multi-island genetic algorithm /
- sequential quadratic programming

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图 1 弹目相对运动示意图

Figure 1. Schematic diagram of relative motion between missile and target

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图 2 空面导弹典型飞行过程

Figure 2. Typical flight process of air-to-surface missile

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图 3 全空域弹道2级优化框架

Figure 3. Two-level optimization framework for trajectory optimization in full airspace

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图 4 传统GA和MIGA对比

Figure 4. Comparison between traditional GA and MIGA

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图 5 全空域弹道优化流程

Figure 5. Flowchart of trajectory optimization in full airspace

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图 6 平均速度迭代收敛结果

Figure 6. Iteration convergence results history of average velocity

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图 7 2种发射条件下优化前后弹道曲线

Figure 7. Trajectory before and after optimization under two launch conditions

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图 8 2种发射条件下优化前后速度曲线

Figure 8. Velocity curve before and after optimization under two launch conditions

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表 1 典型发射条件

Table 1. Typical launch conditions

条件	H_m0 /m	Ma_m0	x_t0 /m	y_t0 /m
发射条件 1	10000	0.8	120000	5
发射条件 2	15000	1.5	200000	5

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表 2 优化前后各个阶段的平均速度对比

Table 2. Comparison of average velocity for each stage before and after optimization

条件		平均速度/(m·s^{− 1})
条件		初制导段	中制导段	末制导段
发射条件 1	优化前	440.80	859.68	537.01
发射条件 1	优化后	461.81	922.67	574.77
发射条件 2	优化前	606.81	1175.84	673.88
发射条件 2	优化后	624.51	1254.72	836.02

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表 3 代理模型误差分析

Table 3. Error analysis for surrogate model

发射高度H_m0/km	参数	R²	RMSE
1~5	θ_Δ	0.952	0.095
1~5	θ_f	0.912	0.114
5~10	θ_Δ	0.953	0.101
5~10	θ_f	0.892	0.065
10~15	θ_Δ	0.962	0.058
10~15	θ_f	0.903	0.114
15~20	θ_Δ	0.961	0.066
15~20	θ_f	0.915	0.088

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