动态事件触发下一般线性多智能体系统完全分布式一致性控制

doi:10.12382/bgxb.2023.0664

摘要/Abstract

摘要：

针对事件触发下一般线性多智能体系统一致性问题研究中,现有算法多采用静态触发方式、一致性协议设计中存在全局信息、无法实现完全分布式控制的问题,提出一种基于动态事件触发的一致性协议。通过引入时变耦合权重自适应地调整图的标量增益,实现一致性问题的完全分布式求解。针对事件触发控制中需要持续检测触发条件的问题,引入基于个体的状态估计器,用于在触发间隔期内对自身状态进行估计,减少了智能体间的通信次数。基于所选择的Lyapunov函数,设计一种耦合系统状态估计值、测量误差和自适应参数的动态触发函数,显著地减少了系统的触发次数、降低了系统的能耗。数值计算表明所提一致性协议能够实现渐近一致性,且不发生Zeno行为。仿真结果表明所提协议能有效降低系统能耗、节省通信资源。

关键词: 多智能体系统, 一致性问题, 一般线性系统, 动态事件触发, 完全分布式算法

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

中图分类号:

TP13

张继雄, 李宗刚, 宁小刚, 陈引娟. 动态事件触发下一般线性多智能体系统完全分布式一致性控制[J]. 兵工学报, 2023, 44(S2): 223-234.

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.

图/表 15

图1 智能体间无向通信拓扑

Fig.1 Undirected communication topology among agents

图2 一致性误差

Fig.2 Consensus error

图3 时变耦合权重

Fig.3 Time-varying coupling weight

图4 内部动态变量

Fig.4 Internal dynamic variables

图5 智能体vi误差和动态触发阈值的变化曲线

Fig.5 Curves of error and dynamic trigger threshold

图6 动态事件触发机制下各智能体的触发时刻

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

图7 总事件触发次数随参数γ的变化曲线

Fig.7 Total event trigger times vs. parameter γ

图8 总事件触发次数随参数χi的变化曲线

Fig.8 Total event trigger times vs. parameter χi

图9 总事件触发次数随参数θi的变化曲线

Fig.9 Total event trigger times vs. parameter θi

图10 总事件触发次数随参数βi的变化曲线

Fig.10 Total event trigger times vs. parameter βi

图11 总事件触发次数随内部动态变量的初值 ηi(0)的变化曲线

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

图12 总事件触发次数随自适应参数初值cij(0)的变化曲线

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

表1 触发次数对比

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

图13 动态和静态触发机制下各智能体的触发时刻

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

表2 通信次数对比

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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