Distributed Cooperative Control of Swarm Unmanned Aerial Vehicles Based on Multi-route Clustering

doi:10.16182/j.issn1004731x.joss.25-0848

Abstract

Abstract:

To address the collaborative control and formation maintenance problems of large-scale swarm unmanned aerial vehicles, a distributed cooperative control method for unmanned aerial vehicles based on multi-route clustering was proposed. Leveraging the characteristics of multiple routes, the overall cooperative control problem of the swarm was decoupled into the cooperative control within the same route and the consensus control between multiple routes. Within the same route, a forward-neighbor information interaction mechanism was designed to determine the neighbor relationships, and a velocity-guided cooperative algorithm was utilized to achieve the desired velocity matching and spacing maintenance of unmanned aerial vehicles; between multiple routes, an adjacent-route leader unmanned aerial vehicle information interaction mechanism was designed; a distributed leader election strategy was established; formation consensus variables were designed by the leader according to the route type, and a consensus algorithm was utilized to maintain the desired formation of the swarm. To quantitatively evaluate the cooperative control effect of the swarm, the intra-route neighbor spacing, order parameter, route tracking error, and formation consensus error were introduced as evaluation indicators. Simulation results show that this method enables large-scale swarm unmanned aerial vehicles to quickly achieve an orderly state during the assembly phase and effectively maintains the desired formation during the mission flight phase, which significantly improves the cooperative efficiency and has excellent scalability.

Key words: swarm unmanned aerial vehicle, multi-route clustering, information interaction mechanism, distributed leader election, velocity coordination, formation consensus

CLC Number:

V279

Xiong Yuyang, Li Chuntao, Qiu Wenhao. Distributed Cooperative Control of Swarm Unmanned Aerial Vehicles Based on Multi-route Clustering[J]. Journal of System Simulation, 2026, 38(7): 1832-1848.

Figures/Tables 27

Fig. 1

Fig. 2

Fig. 3

Fig. 4

Fig. 5

Fig. 6

Table 1

Simulation parameter settings

参数	数值
无人机总数 $N$	72/120
邻居期望间距 $d r$ / $m$	100
位置协同衰减系数 $λ$	5
前向视野范围 $ω$ / $(°)$	60
速度项增益 $(k p, k v, k ξ, k α)$	(5,1,1,10)
水平速度限制 $(V m i n, V m a x)$ /( $m / s$ )	(50,200)
垂直速度限制 $(V z, m i n, V z, m a x)$ /( $m / s$ )	(-5,5)
目标速度 $v *$ /( $m / s$ )	120
自驾仪系数 $(τ V, τ ψ, τ z ˙, τ z)$	(2,1,1,5)

Table 1

Table 2

Fig. 7

Fig. 8

Fig. 9

Fig. 10

Table 3

Table 4

Table 5

Reference circular-arc trajectory during the mission flight phase

航段

圆心/m

半径/m

角度/rad

第2段

(10 000,

1 000,500)

1 300:100:2000

(- π / 2, π / 2)

第4段

(0,1 000,500)

1 300:100:2000

(π / 2, 3 π / 2)

Table 5

Fig. 11

Fig. 12

Fig. 13

Fig. 14

Fig. 15

Fig. 16

Fig. 17

Table 6

Fig. 18

Fig. 19

Fig. 20

Table 7

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航线	航点1	航点2
Ⅰ	(0,-300,500)	(5 000,-300,500)
Ⅱ	(0,-400,500)	(5 000,-400,500)
Ⅲ	(0,-500,500)	(5 000,-500,500)
Ⅳ	(0,-600,500)	(5 000,-600,500)
Ⅴ	(0,-700,500)	(5 000,-700,500)
Ⅵ	(0,-800,500)	(5 000,-800,500)
Ⅶ	(0,-900,500)	(5 000,-900,500)
Ⅷ	(0,-1 000,500)	(5 000,-1 000,500)

航线	航点	航段
航线	航点	第1段	第3段
Ⅰ	1	(0,-300,500)	(10 000,2 300,500)
Ⅰ	2	(10 000,-300,500)	(0,2 300,500)
Ⅱ	1	(0,-400,500)	(10 000,2 400,500)
Ⅱ	2	(10 000,-400,500)	(0,2 400,500)
Ⅲ	1	(0,-500,500)	(10 000,2 500,500)
Ⅲ	2	(10 000,-500,500)	(0,2 500,500)
Ⅳ	1	(0,-600,500)	(10 000,2 600,500)
Ⅳ	2	(10 000,-600,500)	(0,2 600,500)
Ⅴ	1	(0,-700,500)	(10 000,2 700,500)
Ⅴ	2	(10 000,-700,500)	(0,2 700,500)
Ⅵ	1	(0,-800,500)	(10 000,2 800,500)
Ⅵ	2	(10 000,-800,500)	(0,2 800,500)
Ⅶ	1	(0,-900,500)	(10 000,2 900,500)
Ⅶ	2	(10 000,-900,500)	(0,2 900,500)
Ⅷ	1	(0,-10 00,500)	(10 000,3 000,500)
Ⅷ	2	(10 000,-1 000,500)	(0,3 000,500)

评价指标	数量规模N
评价指标	72	120
序参量最小值	0.996 5	0.995 8
同航线邻居间距偏差参考值/m	44	46
航线距离差值偏差参考值/m	25	28
多航线头机队形一致差偏差参考值/(°)	0.9	1

算法	速度收敛时间	序参量收敛时间
本文	40	3
文献[14]	46	8