A Novel Approach for Fast Average Consensus Under Unreliable Communication in Distributed Multi Agent Networks

Abstract

In this research an algorithm is proposed to find the total number of agents participating in a multi agent network. Also to achieve hasten distributed average consensus in order to consider a network with reliable and unreliable communication links. Class of algorithm is considered in which fixed initial state values are assigned to all agents in the network, with the iterations they updates their initial values by communicating with their neighboring agents within a multi agent network. Algorithm with weighted matrix satisfy the convergence condition of average consensus and accelerate the method to achieve the consensus. Usually this convergence process is relatively sluggish and take moreover numerous iterations to achieve a consensus. To overcome the above issues, a new approach is proposed in order to minimize the rate of convergence. A two step algorithm has been proposed, where in step one each agent employs a linear predictor to predict future agent values. In second step the computed values are used to proceed further by the other agents to achieve consensus in order to bypass the redundant states. In the end proposed algorithm is compared with other existing consensus frameworks to strengthen the claim regarding the proposed two step algorithm which leads to escalate the rate of convergence and reduces the number of iterations.