基于改进粒子群算法的飞行器协同轨迹规划

周宏宇; 王小刚; 单永志; 赵亚丽; 崔乃刚

doi:10.16383/j.aas.c190865

基于改进粒子群算法的飞行器协同轨迹规划

doi: 10.16383/j.aas.c190865

1.
哈尔滨工业大学航天学院哈尔滨 150001
2.
国营六二四厂哈尔滨 150030
3.
北京航天晨信科技有限公司北京 102308

基金项目: 中国博士后科学基金(2019M661290)资助

详细信息

作者简介:
周宏宇：哈尔滨工业大学讲师. 主要研究方向为飞行器轨迹优化.E-mail: sd4574462@foxmail.com

王小刚：哈尔滨工业大学副教授. 主要研究方向为飞行器制导, 导航与控制. 本文通信作者.E-mail: wangxiaogang@hit.edu.com

单永志：哈尔滨国营六二四厂研究员. 主要研究方向为飞行器总体设计.E-mail: yongzhi0451@sina.com

赵亚丽：北京航天晨信科技有限公司研究员. 主要研究方向为优化设计与系统工程.E-mail: zhaoyali_aerocim@163.com

崔乃刚：哈尔滨工业大学教授. 主要研究方向为飞行器制导, 导航与控制.E-mail: cui_naigang@163.com

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出版历程
- 收稿日期: 2019-12-20
- 网络出版日期: 2022-10-25
- 刊出日期: 2022-11-22

Synergistic Path Planning for Multiple Vehicles Based on an Improved Particle Swarm Optimization Method

1.
School of Astronautics, Harbin Institute of Technology, Harbin 150001
2.
State-owned Factory No. 624, Harbin 150030
3.
Beijing Aerocim Technology Co., Ltd., Beijing 102308

Funds: Supported by National Postdoctoral Science Foundation of China (2019M661290)

More Information

Author Bio:
ZHOU Hong-Yu　Lecturer at Harbin Institute of Technology. His research interest covers trajectory op-timization of vehicles

WANG Xiao-Gang　Associate professor at Harbin Institute of Technology. His research interest covers guidance, navigation and guidance of vehicles. Corresponding author of this paper

SHAN Yong-Zhi　Professor at State-owned Factory No. 624. His main research interest is overall design of vehicles

ZHAO Ya-Li　Professor at Beijing Aerocim Technology Co., Ltd.. Her research interest covers optimization design and system engineering

CUI Nai-Gang　Professor at Harbin Institute of Technology. His research interest covers guidance, naviga-tion and guidance of vehicles

摘要

摘要: 考虑气动、轨迹、约束、指标间的耦合关系, 以多高超声速飞行器同时到达为目标建立了协同规划模型; 设计了一种自动满足终端约束的全新滑翔飞行剖面, 减少了规划算法需要处理的约束数量; 推导了滑翔段高精度解析解, 实现了过程约束和性能指标的快速求解; 提出了一种改进粒子群优化(Particle swarm optimization, PSO)算法, 借助强化学习方法构建协同需求与惯性权重间的动态映射网络, 提高了在线规划效率. 最后通过数学仿真验证了方法的正确性和有效性.
- 高超声速飞行器 /
- 协同轨迹规划 /
- 粒子群优化 /
- 强化学习
Abstract: This paper researches the synergistic flight for multiple hypersonic vehicles. The synergistic planning problem is formulated in view of the nonlinear coupling among aerodynamics, the performance index, and the path constraints. Then, the gliding profile, which naturally satisfies the terminal constraints and decreases the constraints, is proposed. Meanwhile, accurate solutions are deduced in the glide phase, so path constraints and the performance index can be quickly derived. An improved particle swarm optimization (PSO) method is developed by building the network between synergistic requirements and the optimal inertial weight in PSO based on a reinforcement learning method. Thus, the efficiency online computational efficiency can be largely improved. Numerical simulation results indicate the efficiency of the proposed method.
- Hypersonic vehicle /
- synergistic path planning /
- particle swarm optimization (PSO) /
- reinforcement learning

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图 1 在线规划算法流程图

Fig. 1 The flowchart of the planning algorithm

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图 2 滑翔段飞行剖面

Fig. 2 Flight profiles of different vehicles

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图 3 飞行路径角随时间变化情况

Fig. 3 Time histories of the flight path angle

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图 4 过程约束随时间变化情况

Fig. 4 Time histories of the path constraints

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图 5 攻角和倾侧角随时间变化情况

Fig. 5 Histories of angle-of-attack and the bank angle

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图 6 攻角随时间变化情况(飞行器1)

Fig. 6 Histories of the angle-of-attack (Vehicle 1)

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图 7 动压随时间变化情况(飞行器1)

Fig. 7 Histories of the dynamic pressure (Vehicle 1)

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表 1 初始状态和终端约束

Table 1 The initial states and the terminal constraints

初始值	经纬度 (°)	高度 (km)	速度 (m/s)	飞行路径角 (°)	剩余航程 (km)
飞行器 1	E 90, N 45	56	5400	−2.0	2304
飞行器 2	E 65, N 30	54	5300	−2.0	2263
飞行器 3	E 40, N 35	52	5200	−1.0	2324
飞行器 4	E 70, N 70	50	5100	−1.0	2285
终端值	E 60, N 50	25	—	−1.0	0

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表 2 基本PSO和改进PSO计算效率对比

Table 2 Comparison of the computation efficiency

进化代数	最大值	最小值	平均值	标准差
基本 PSO	20	8	14	3.17
改进 PSO	16	6	10	2.69

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表 3 滑翔段干扰因素设置

Table 3 Disturbances in the glide phase

序号	干扰因素	$3\sigma $值
1	初始速度 (m/s)	30
2	初始飞行路径角 (°)	0.2
3	初始高度 (m)	500
4	初始航向偏差 (°)	0.2
5	初始剩余航程 (km)	50
6	气动系数 (%)	10
7	大气密度 (%)	10

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