Prescribed-time Formation Control with Event-triggering Mechanism for Multi-agent Systems

doi:10.12382/bgxb.2024.0292

Abstract

Abstract:

To solve the problems of slow convergence rate and continuous controller updates in formation control, this paper proposes a prescribed-time formation control method based on event-triggering mechanism for second-order multi-agent systems.Based on a prescribed-time acceleration observer, the followers can estimate the acceleration state of leader within the prescribed time.Moreover, a prescribed-time formation controller based on the event-triggering mechanism is designed to enable the followers to keep up with the leader within the prescribed time.The event-triggering mechanism proposed avoids the continuous updates of controller.Through the rigorous theoretical analysis, it is proved that the proposed method can be used to achieve the prescribed-time formation control for the multi-agent systems without Zeno behavior.Simulated results indicate that the proposed prescribed-time formation control method based on the event-triggering mechanism can make the multi-agent systems form a desired formation configuration within a preset time, and reduce the update frequency of controller to save resources.The feasibility and extensibility of the proposed control method are further verified based on the formation flight test results of quadrotor UAVs.

Key words: multi-agent system, formation control, prescribed-time control, event-triggering mechanism, quadrotor unmanned aerial vehicle

CLC Number:

TP13

HOU Tianle, BI Wenhao, HUANG Zhanjun, LI Minghao, ZHANG An. Prescribed-time Formation Control with Event-triggering Mechanism for Multi-agent Systems[J]. Acta Armamentarii, 2025, 46(4): 240292-.

Figures/Tables 20

Fig.1 Communication topology (Example 1)

Table 1 The initial states of MASs (Example 1)

智能体	p_i/m	v_i/(m·s^-1)	a_i/(m·s^-2)
0	-1	0
1	10	-1	0
2	2	-2	0.5
3	-10	3	0
4	5	1	1
5	-3	2	-1
6	3	-3	0

Fig.2 Estimated acceleration trajectories of agents

Fig.3 Position tracking error trajectories of followers

Fig.4 Velocity tracking error trajectories of followers

Fig.5 Control input trajectories of followers

Table 2 Comparison of update frequencies of controller with and without event-triggering mechanism (Example 1)

类别	跟随者智能体
类别	1	2	3	4	5	6
采用事件触发机制	999	853	843	964	860	970
未采用事件触发机制	5000

Table 3 Algorithm parameters (Example 2)

算法	算法参数
文献[32]算法	k₁=1.2,k₂=0.3,k₃=0.5,k₄=0.8,k₅=3.5,k₆=2,a=1,b=2,β=1.5,l₁=0.1,l₂=0.3,ρ=0.2
本文算法	k₁=1,δ=1,h₁=2,α₁=2,k₂=30,k₃=15,h₂=5,α₂=8,γ=5,T₁=0.5

Fig.6 Total tracking error trajectories (Example 2)

Fig.7 Control input trajectories of followers (Example 2)

Fig.8 Total tracking error trajectories (Example 3)

Table 4 Comparison of update frequencies of controller with and without event-triggering mechanism (Example 3)

算法	跟随者智能体
算法	1	2	3	4	5	6
本文算法	707	694	663	680	638	663
文献[24]算法	3000

Fig.9 Hardware platform

Fig.10 Communication topology of quadrotor UAVs

Table 5 The initial position and velocity of UAVs

无人机	p_i/m	v_i/(m·s^-1)	a_i/(m·s^-2)
0	[0,1,0]^T	[0,0,0]^T
1	[2.5,1,0]^T	[0,0,0]^T	[0,0,0]^T
2	[2.5,-1.5,0]^T	[0,0,0]^T	[0,0,0]^T
3	[0,-1.5,0]^T	[0,0,0]^T	[0,0,0]^T

Fig.11 formation flight

Fig.12 Flight trajectories

Fig.13 Position tracking trajectories

Fig.14 Velocity tracking trajectories

Table 6 The triggering frequency with the event triggering mechanism

跟随者无人机	x轴方向	y轴方向	z轴方向
1	251	264	243
2	323	288	281
3	312	292	284

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