JOURNAL ARTICLE

Event-Triggered Control for Consensus of Multiagent Systems With Fixed/Switching Topologies

Zheng‐Guang WuYong XuRenquan LuYuanqing WuTingwen Huang

Year: 2017 Journal:   IEEE Transactions on Systems Man and Cybernetics Systems Vol: 48 (10)Pages: 1736-1746   Publisher: Institute of Electrical and Electronics Engineers

Abstract

In this paper, the leader-following consensus problem of high-order multiagent systems via event-triggered control is discussed. A novel distributed event-triggered communication protocol based on state estimates of neighboring agents is proposed to solve the consensus problem of the leader-following systems. We first investigate the consensus problem in a fixed topology, and then extend to the switching topologies. State estimates in fixed topology are only updated when the trigger condition is satisfied. However, state estimates in switching topologies are renewed with two cases: 1) the communication topology is switched or 2) the trigger condition is satisfied. Clearly, compared to continuous-time interaction, this protocol can greatly reduce the communication load of multiagent networks. Besides, the event-triggering function is constructed based on the local information and a new event-triggered rule is given. Moreover, "Zeno behavior" can be excluded. Finally, we give two examples to validate the feasibility and efficiency of our approach.

Keywords:
Network topology Multi-agent system Computer science Control (management) Event (particle physics) Consensus Distributed computing Topology (electrical circuits) Computer network Mathematics Artificial intelligence Physics

Metrics

423
Cited By
50.00
FWCI (Field Weighted Citation Impact)
52
Refs
1.00
Citation Normalized Percentile
Is in top 1%
Is in top 10%

Citation History

Topics

Distributed Control Multi-Agent Systems
Physical Sciences →  Computer Science →  Computer Networks and Communications
Stability and Control of Uncertain Systems
Physical Sciences →  Engineering →  Control and Systems Engineering
Reinforcement Learning in Robotics
Physical Sciences →  Computer Science →  Artificial Intelligence

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