Asynchronous consensus of multi-agent systems via variable period intermittent control with heterogeneous pulse-modulated function

Most existing consensus control in multi-agent systems ( MAS s) require agents to update their state synchronously, which means that some agents need to wait for all individuals to complete the iteration before starting the next iteration. To overcome this bottleneck, this paper studied asynchronous consensus problems of second-order MAS s ( SOMAS s) with aperiodic communication. An asynchronous pulsemodulated intermittent control ( APIMC ) with heterogeneous pulse-modulated function and time-varying control period, which can unify impulsive control and sampled-data control, is proposed for the consensus of SOMAS s. A time-varying discrete system is constructed to describe the evolution of the sample values of position and velocity of the SOMAS . Then, by the analysis tools from the stochastic matrix and the properties of the Laplace matrix of graph, some effective conditions are obtained to show the relationship between the convergence of the controlled SOMAS s and the control parameters. Finally, a 300-node SOMAS whose topology is a random geographic network is included to verify the feasibility of the proposed control and the correctness of the theoretical analysis. & COPY; 2022 The Franklin Institute. Published by Elsevier Ltd. All rights reserved.

Automated summary

This paper studies the asynchronous consensus problems of second-order multi-agent systems with aperiodic communication. An asynchronous pulse-modulated intermittent control method is proposed to overcome the waiting problem caused by synchronous state updates in existing systems. By constructing a time-varying discrete system to describe the evolution of sample values and analyzing the properties of stochastic and Laplace matrices, effective conditions are obtained to describe the relationship between the convergence of controlled systems and control parameters. Finally, a 300-node multi-agent system with a random geographic network is used to verify the feasibility of the proposed control and the correctness of the theoretical analysis.