基于LDE-MADDPG算法的无人机集群编队集结控制策略

doi:10.16182/j.issn1004731x.joss.24-0333

摘要/Abstract

摘要：

针对MADDPG算法用于无人机集群编队集结控制的局限性，提出基于LDE-MADDPG算法的无人机集群编队集结控制策略。通过设计状态特征学习网络和解耦式Critic网络提出LDE-MADDPG算法，用以改善MADDPG算法的泛化性、可扩展性及集群训练效率。将该算法结合构建的解耦式奖励函数、集群状态空间和无人机动作空间等要素，生成了能够适应不同队形和不同数量的无人机集群编队集结策略。仿真实验表明：较MADDPG算法，LDE-MADDPG算法提升了19.6%的训练效率；生成的集群编队集结控制策略能够在60 s内完成包括“菱形”在内的6种无人机队形集结，80 s内实现从6~21架次的无人机集群编队集结，表现出了良好的泛化性和可扩展性。

关键词: LDE-MADDPG算法, 状态特征学习网络, 解耦式Critic网络, 编队集结

Abstract:

To solve the problem of difficulty in UAV cluster formation rendezvous based on MADDPG algorithm, an autonomous collaborative control strategy based on LDE-MADDPG algorithm is proposed. To address the issues of weak generalization, poor scalability, and slow cluster training process of MADDPG algorithm, LDE-MADDPG algorithm was proposed by designing a state feature learning network and a decoupled Critical network. By integrating LDE-MADDPG algorithm with strategy generation elements such as the decoupled reward function, cluster state space, and UAV action space, a control strategy for UAV cluster formation endezvous that can adapt to diverse formations and varying quantities has been developed. Simulation experiments show that compared to MADDPG algorithm, LDE-MADDPG algorithm improves the training process by 19.6%; The generated control strategy can complete the assembly of six different formations, such as a diamond, within 60 seconds, and achieve the formation and assembly of 6-21 drone clusters within 80 seconds with good generalization and scalability.

Key words: LDE-MADDPG algorithm, state feature learning network, decoupled Critical network model, formation rendezvous

中图分类号:

V279

肖玮,高甲博,柯学良 . 基于LDE-MADDPG算法的无人机集群编队集结控制策略[J]. 系统仿真学报, 2025, 37(9): 2335-2351.

Xiao Wei,Gao Jiabo,Ke Xueliang . Control Strategy for UAV Cluster Formation Rendezvous Based on LDE-MADDPG Algorithm[J]. Journal of System Simulation, 2025, 37(9): 2335-2351.

图/表 22

图1

图2

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图4

表1

无人机集群编队集结任务强化学习环境实验参数

实验参数	数值
无人机大小/m	$5$
无人机初始速度 $(v x, v y)$ /( $m / s$ )	$(1,0)$
无人机最大速度 $v x m a x 、 v y m a x$ /( $m / s$ )	$5$
无人机最小安全飞行空间距离 $d s a f$ /m	$3$
任务环境大小	$150 m × 80 m$
任务完成规定时间 $t e$ /s	$120$
集结完成时无人机集群与期望集结编队队形允许的最大距离误差 $d e$ /m	$5$

表1

表2

训练超参数

超参数	数值	意义
MaxEpisode	150	训练局数
MaxStep	300	每局总的时间步数
$β$	0.000 1	Actor网络学习率
$α g$	0.001	全局Critic网络学习率
$α$	0.001	局部Critic网络学习率
$γ$	0.95	累计奖励折扣因子
Buffer-size	500 000	经验池中能够存放的样本容量
Batch-size	128	每次训练网络模型的一小批量样本数
$τ$	0.01	目标网络参数软更新频率
$a 1, a 2, a 3, a 4$	0.3,0.3,0.1,0.3	奖励函数调节系数

表2

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图6

图7

表3

表4

图8

图9

图10

图11

图12

表5

图13

图14

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图16

表6

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局数	架次
局数	第1架	第2架	第3架	第4架	第5架
第80局	240	-870	-980	-460	1 550
第140局	15 220	10 110	9 420	10 050	11 980
训练进程	249.7/局	183.0/局	173.3/局	175.2/局	173.8/局
局数	架次				集群
局数	第6架	第7架	第8架	第9架	集群
第80局	-990	240	880	450	6
第140局	-870	10 150	12 320	11 840	10 011
训练进程	2.0/局	165.2/局	190.7/局	189.8/局	166.75/局

局数	架次
局数	第1架	第2架	第3架	第4架	第5架
第80局	1 080	250	5 210	130	9 450
第140局	16 210	12 870	15 890	10 900	18 530
训练进程	252.2/局	210.3/局	178.0/局	179.5/局	153/局
局数	架次				集群
局数	第6架	第7架	第8架	第9架	集群
第80局	280	-980	4 460	7 840	3 080
第140局	16 430	10 880	16 090	17 650	15 050
训练进程	269.2/局	197.7/局	193.8/局	163.5/局	199.5/局

编队队形	距离/m					集结完成时间/s
编队队形	20 s	40 s	60 s	80 s	100 s	集结完成时间/s
10架次三角形	18.458	1.570	0.625	0.625	0.625	60
12架次正方形	17.836	5.753	0.524	0.521	0.521	60
9架次十字形	24.851	3.021	0.259	0.259	0.259	60
8架次心形	16.744	1.189	0.218	0.218	0.218	40
9架次菱形	22.964	1.655	0.133	0.133	0.133	60

架次	距离/m					集结完成时间/s
架次	20 s	40 s	60 s	80 s	100 s	集结完成时间/s
6	26.308	1.080	0.208	0.208	0.208	40
10	31.796	3.281	0.873	0.793	0.727	60
18	47.711	13.990	2.167	0.294	0.294	80
21	47.626	17.839	4.018	0.123	0.123	80