Abstract

An event-based consensus filtering control scheme for multi-agents with multiple mixing delays is proposed in the paper. Firstly, a piece-wise sampling model with transmission delay defined from sensors to controllers is built, and the effect of time-varying delay on sampling is analyzed. Secondly, a self-triggered scheme is proposed to take into consideration of reducing redundant data and complexity. Thirdly, to fully utilize the available information, by employing an improved generalized free-weighting matrix inequality, a novel Lyapunov-Krasovskii functional approach is proposed to achieve global asymptotically synchronization. At last, an example of multiple unmanned aerial vehicles is offered to show the effectiveness of proposed method.

Highlights

  • Multi-agent systems have received considerable attention since the 1990s

  • Based on the study of networked control system theory and computer science, while with analysis of characteristics such as clustering and connecting weights, a huge scale network could be simplified to some smaller subones

  • Ubiquitous transmission delays caused by sensors, controllers, actuators and network transmission, with inherent system delay make the developing of researches and applications slow down

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Summary

Introduction

Multi-agent systems have received considerable attention since the 1990s. Based on the study of networked control system theory and computer science, while with analysis of characteristics such as clustering and connecting weights, a huge scale network could be simplified to some smaller subones. INDEX TERMS Multi-agents, event-triggered, synchronization, time delay, sampled-data control. M. Xu et al.: Event-Based Interaction Sampled-Control for Consensus of Multi-Agents With Multiple Time-Varying Delays stability of linear plant with a sector bounded nonlinear and possibly time-varying. To better make the utmost of hybrid information, in [17], an exponential synchronization criterion in discrete-time communications for CDNs is proposed, with a larger sampling interval, and the number of decision variables is decreased, thereby reducing the computational burden.

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