Fully Distributed Consensus Control of General Linear Multi-agent System Based on Dynamic Event-trigger

doi:10.12382/bgxb.2023.0664

Abstract

Abstract:

For the consensus control problem of general linear multi-agent system under event-trigger, the existing algorithms mostly adopt static triggering method, and there is global information in the design of consensus protocol, which can not realize fully distributed control. A consensus protocol based on dynamic event-triggering is proposed to solve this problem. The scalar gain of the graph is adaptively adjusted to realize the fully distributed solution of consensus problem by introducing the time-varying coupling weight. For the problem of continuously detecting the trigger conditions in event-triggered control, an individual-based state estimator is introduced to estimate its own state during the trigger interval, which reduces the number of communications between agents. Based on the selected Lyapunov function, a dynamic trigger function is designed to couple the state estimation value, measurement error and adaptive parameters of the system, which significantly reduces the number of triggers and the energy consumption of the system. Numerically calculated results show that the proposed consensus protocol can achieve asymptotic consensus without Zeno behavior. Simulated results show that the proposed protocol can effectively reduce system energy consumption and save communication resources.

Key words: multi-agent system, consensus problem, general linear system, dynamic event-trigger, fully distributed algorithm

CLC Number:

TP13

ZHANG Jixiong, LI Zonggang, NING Xiaogang, CHEN Yinjuan. Fully Distributed Consensus Control of General Linear Multi-agent System Based on Dynamic Event-trigger[J]. Acta Armamentarii, 2023, 44(S2): 223-234.

Figures/Tables 15

Fig.1 Undirected communication topology among agents

Fig.2 Consensus error

Fig.3 Time-varying coupling weight

Fig.4 Internal dynamic variables

Fig.5 Curves of error and dynamic trigger threshold

Fig.6 Trigger-time of each agent under dynamic event-triggered mechanism

Fig.7 Total event trigger times vs. parameter γ

Fig.8 Total event trigger times vs. parameter χi

Fig.9 Total event trigger times vs. parameter θi

Fig.10 Total event trigger times vs. parameter βi

Fig.11 Changing curve of the total number of event triggers with the initial value of internal dynamic variable ηi(0)

Fig.12 Changing curve of the total number of event triggers with the initial value of adaptive parameter cij(0)

Table 1 Comparison of trigger times

ETC算法来源	智能体
ETC算法来源	v₁	v₂	v₃	v₄	v₅	v₆	总和
文献[18]	125	177	158	167	109	134	870
文献[21]	81	153	238	177	91	102	842
文献[22]	99	54	124	58	45	63	443
本文算法	52	24	40	20	24	24	184

Fig.13 Trigger-time of each agent under dynamic and static trigger mechanisms

Table 2 omparison of communication times

ETC算法来源	智能体
ETC算法来源	v₁	v₂	v₃	v₄	v₅	v₆	总和
文献[18]ETC	125	177	158	167	109	134	870
本文算法	52	24	40	20	24	24	184

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