多智能体仿射编队机动控制的有向交互拓扑优化设计

马苏慧; 张栋; 王孟阳; 王庭晖; 刘送丹

doi:10.13700/j.bh.1001-5965.2023.0180

多智能体仿射编队机动控制的有向交互拓扑优化设计

doi: 10.13700/j.bh.1001-5965.2023.0180

马苏慧¹,
张栋^{1, 2, ,},
王孟阳¹,
王庭晖¹,
刘送丹³

1.
西北工业大学航天学院，西安 710072
2.
陕西省空天飞行器设计重点实验室，西安 710072
3.
中国航天科技创新研究院，北京 100083

基金项目: 国家自然科学基金(61933010)

详细信息

通讯作者:
E-mail：zhangdong@nwpu.edu.cn

中图分类号: TP273；N945.15
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- 文章访问数: 290
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- PDF下载量: 25
- 被引次数: 0
出版历程
- 收稿日期: 2023-04-14
- 录用日期: 2023-08-10
- 网络出版日期: 2023-09-05
- 整期出版日期: 2025-04-30

Directed interactive topology optimization design for multi-agent affine formation maneuver control

1.
School of Astronautics，Northwestern Polytechnical University，Xi’an 710072，China
2.
Shaanxi Key Laboratory of Aerospace Flight Vehicle Technology，Xi’an 710072，China
3.
China Academy of Aerospace Science and Innovation，Beijing 100083，China

Funds: National Natural Science Foundation of China (61933010)

More Information

Corresponding author: E-mail：zhangdong@nwpu.edu.cn

摘要

摘要:
研究了多智能体仿射编队机动控制的有向交互拓扑优化设计问题。考虑信息交互成本、信息传播能耗等优化指标，建立了包含拓扑结构构造和权重配置的仿射编队机动有向拓扑优化模型；针对仿射编队机动拓扑结构构造，提出了有向k根图的检测方法，实现了有向信息交互拓扑的d+1根约束求解，并设计了一种改进NSGA-II的拓扑结构构造优化算法，以二维空间中7个智能体的编队为例进行仿真验证，结果表明：改进NSGA-II的拓扑结构构造优化算法具有更好的优化效果，能够有效地为仿射编队机动控制提供多种可行的有向交互拓扑，并且所生成的交互拓扑能够满足有向d+1根图的要求。
- 多智能体系统 /
- 仿射变换 /
- 编队控制 /
- 交互拓扑设计 /
- 有向图 /
- 多目标优化
Abstract:
This paper investigated the directed interactive topology optimization design problem for multi-agent affine formation maneuver control. Firstly, by considering the optimization indexes such as information interaction cost and energy consumption during information spreading, a directed topology optimization model for affine formation maneuver was established, including topology structure construction and weight allocation. Secondly, in view of the topological structure construction for affine formation maneuver, a directed k-rooted graph detection method was proposed, which could realize the solution of d + 1-rooted constraint for directed information interaction topology. Then, an improved NSGA-II-based topology structure construction optimization algorithm was designed. Finally, a formation of seven agents in two-dimensional space was taken as an example for simulation verification. The results show that the improved NSGA-II-based topology structure construction optimization algorithm has better optimization effects. It can effectively provide a variety of feasible directed interactive topologies for affine formation maneuver control, and the generated interactive topology can meet the requirements of a directed d + 1-rooted graph.
- multi-agent system /
- affine transformation /
- formation control /
- interactive topology design /
- directed graph /
- multi-objective optimization

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图 1 表1对应的拓扑结构

Figure 1. Topology structure corresponding to Table 1

下载: 全尺寸图片幻灯片

图 2 编队轨迹

Figure 2. Formation trajectory

下载: 全尺寸图片幻灯片

图 3 总编队误差范数

Figure 3. Total formation error norm

下载: 全尺寸图片幻灯片

表 1 拓扑结构构造结果

Table 1. Topology structure construction results

拓扑编号	$ {f_1} $	$ {f_2} $	${f_3}$	${f_4}$
${{\mathcal{G}}_1}$	12	43.55	6.00	1.25
${{\mathcal{G}}_2}$	12	43.55	4.83	1.50
${{\mathcal{G}}_3}$	12	44.21	4.00	1.25
${{\mathcal{G}}_4}$	12	44.69	3.50	1.50
${{\mathcal{G}}_5}$	12	44.93	4.00	1.00
${{\mathcal{G}}_6}$	12	45.20	2.50	1.75
${{\mathcal{G}}_7}$	12	46.15	2.00	2.00.
${{\mathcal{G}}_8}$	13	48.73	3.33	1.50
${{\mathcal{G}}_9}$	13	50.15	1.83	2.00
${{\mathcal{G}}_{10}}$	13	50.81	1.67	2.00
${{\mathcal{G}}_{11}}$	13	52.00	1.00	2.50
${{\mathcal{G}}_{12}}$	13	52.00	1.50	2.25
${{\mathcal{G}}_{13}}$	14	47.74	3.50	1.00
${{\mathcal{G}}_{14}}$	14	53.63	2.33	1.75
${{\mathcal{G}}_{15}}$	14	53.87	2.50	1.50
${{\mathcal{G}}_{16}}$	14	54.81	1.33	2.25
${{\mathcal{G}}_{17}}$	14	56.47	1.17	2.25
${{\mathcal{G}}_{18}}$	14	58.32	0.50	2.75
${{\mathcal{G}}_{19}}$	14	59.85	0.00	3.00
${{\mathcal{G}}_{20}}$	15	53.40	3.00	1.25
${{\mathcal{G}}_{21}}$	15	60.00	0.33	2.75
${{\mathcal{G}}_{22}}$	15	60.47	0.83	2.50
${{\mathcal{G}}_{23}}$	15	62.47	0.67	2.50
${{\mathcal{G}}_{24}}$	16	59.05	2.00	1.50
${{\mathcal{G}}_{25}}$	18	68.00	1.83	1.75
${{\mathcal{G}}_{26}}$	18	68.00	1.83	1.75
${{\mathcal{G}}_{27}}$	20	75.48	1.33	2.00
${{\mathcal{G}}_{28}}$	20	77.14	1.00	2.00

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表 2 算法评估指标结果

Table 2. Algorithm evaluation indicator results

算法	${N_{{\text{PS}}}}$ 平均值	${N_{{\text{PSMF}}}}$ 平均值	IGDX		IGDF		暴力搜索
算法	${N_{{\text{PS}}}}$ 平均值	${N_{{\text{PSMF}}}}$ 平均值	平均值	方差	平均值	方差	暴力搜索
imNSGA-II	2.15	0.05	3.1857	4.9742	3.7809	423.7237	37
本文	23.16	0.58	2.3401	3.4508	1.3760	5.8359	15

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