Weak average consensus of multi-agent system with partial flowing nodes

2016 ◽  
Vol 39 (8) ◽  
pp. 1216-1224 ◽  
Author(s):  
Jinping Mou ◽  
Huafeng Ge ◽  
Jie Wang
Keyword(s):  
Lyapunov Function ◽  
Iterative Method ◽  
Consensus Problem ◽  
Multi Agent System ◽  
Agent System ◽  
Numerical Example ◽  
Common Value ◽  
Average Consensus ◽  
Multi Agent

A weak average consensus problem is investigated for a distributed multi-agent system with partial flowing nodes (MASPFN), which is composed of subsystem I and subsystem II: subsystem I consists of a fixed agent set, whereas subsystem II consists of a varying agent set in which some agents may join in or quit from the multi-agent system with partial flowing nodes randomly. The weak average consensus refers to the fact that states of all agents in subsystem I approach a common value under the influence of subsystem II. Based on the Lyapunov function and the iterative method, several consensus criteria are obtained. One numerical example shows the reliability of the proposed methods.

Adaptive Consensus Problem of Leader-Follower Multi-Agent System

2010 ◽  
Vol 27 (10) ◽  
pp. 100501 ◽  
Author(s):  
Zhang Qing ◽  
Chen Shi-Hua ◽  
Guo Wan-Li
Keyword(s):  
Consensus Problem ◽  
Multi Agent System ◽  
Agent System ◽  
Multi Agent

scholarly journals Supportive consensus

PLoS ONE ◽  
2020 ◽  
Vol 15 (12) ◽  
pp. e0243215
Author(s):  
A. Palomares ◽  
M. Rebollo ◽  
C. Carrascosa
Keyword(s):  
Consensus Algorithm ◽  
Consensus Problem ◽  
Multi Agent System ◽  
Agent System ◽  
Acceptable Solution ◽  
Consensus Algorithms ◽  
Boundary Constraints ◽  
Average Value ◽  
Multi Agent

The paper is concerned with the consensus problem in a multi-agent system such that each agent has boundary constraints. Classical Olfati-Saber’s consensus algorithm converges to the same value of the consensus variable, and all the agents reach the same value. These algorithms find an equality solution. However, what happens when this equality solution is out of the range of some of the agents? In this case, this solution is not adequate for the proposed problem. In this paper, we propose a new kind of algorithms called supportive consensus where some agents of the network can compensate for the lack of capacity of other agents to reach the average value, and so obtain an acceptable solution for the proposed problem. Supportive consensus finds an equity solution. In the rest of the paper, we define the supportive consensus, analyze and demonstrate the network’s capacity to compensate out of boundaries agents, propose different supportive consensus algorithms, and finally, provide some simulations to show the performance of the proposed algorithms.

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