Effect of network structure to the convergence rate of agents in multi-agent systems

2017 ◽  
Author(s):  
Shasha Feng ◽  
Li Wang ◽  
Shiwen Sun ◽  
Chengyi Xia
Keyword(s):  
Convergence Rate ◽  
Network Structure ◽  
Multi Agent Systems ◽  
Agent Systems ◽  
Multi Agent

A generalized hierarchical nearly cyclic pursuit for the leader-following consensus problem in multi-agent systems

2017 ◽  
Vol 40 (5) ◽  
pp. 1529-1537 ◽  
Author(s):  
Muhammad Iqbal ◽  
John Leth ◽  
Trung D Ngo
Keyword(s):  
Convergence Rate ◽  
Consensus Problem ◽  
Multi Agent Systems ◽  
Agent Systems ◽  
Cyclic Pursuit ◽  
Leader Following ◽  
Multi Agent ◽  
Simulation Results ◽  
Pursuit Strategy

In this paper, we solve the leader-following consensus problem using a hierarchical nearly cyclic pursuit (HNCP) strategy for multi-agent systems. We extend the nearly cyclic pursuit strategy and the two-layer HNCP to the generalized L-layer HNCP that enables the agents to rendezvous at a point dictated by a beacon. We prove that the convergence rate of the generalized L-layer HNCP for the leader-following consensus problem is faster than that of the nearly cyclic pursuit. Simulation results demonstrate the effectiveness of the proposed method.

scholarly journals On Convergence Rate of Leader-Following Consensus of Linear Multi-Agent Systems With Communication Noises

2016 ◽  
Vol 61 (11) ◽  
pp. 3586-3592 ◽  
Author(s):  
Long Cheng ◽  
Yunpen Wang ◽  
Wei Ren ◽  
Zeng-Guang Hou ◽  
Min Tan
Keyword(s):  
Convergence Rate ◽  
Multi Agent Systems ◽  
Agent Systems ◽  
Leader Following ◽  
Multi Agent

Distributed consensus of multi-agent systems with increased convergence rate

2020 ◽  
Vol 0 (0) ◽  
Author(s):  
Ke-cai Cao ◽  
Yun Chai ◽  
Chenglin Liu
Keyword(s):  
Convergence Rate ◽  
Finite Time ◽  
Nearest Neighbor ◽  
Laplacian Matrix ◽  
Consensus Problem ◽  
Multi Agent System ◽  
Multi Agent Systems ◽  
Consensus Protocol ◽  
Agent Systems ◽  
Multi Agent

AbstractConsensus problem with faster convergence rate of consensus problem has been considered in this paper. Adding more edges such as that connecting each agent and its second-nearest neighbor or changing the consensus protocol such as mixing asymptotic terms and terms of finite-time has been proved to be possible ways in increasing the convergence rate of multi-agent system in this paper. Based on analysis of Laplacian matrix, increasing of the convergence rate has been proved using the second-smallest eigenvalue for the first method. Concerning the second method, advantages of asymptotic consensus protocol and finite-time consensus protocol have been mixed together with the help of homogeneity function and theory of Lyapunov. Simulation results using matlab are also presented to illustrate the newly designed consensus protocols in increasing the convergence rate.

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