Consensus and Convergence Rate Optimization in Distributed Drone Networks in The Presence of Random Noise

Distributed consensus networks are systems that use calculations and local communication between network nodes in order to reach a common value. In these networks, consensus means reaching an agreement on a specific value of interest that depends on the state of all agents. The consensus algorithm is a protocol that determines the exchange of information between an agent and all its neighbors in the network. In this research, while expressing the concept of consensus in distributed networks, we have tried to express the effects of adverse factors (noise, fading …) on consensus algorithms. Also in this research, considering the vectored signals, average consensus control for discrete-time networks with first-order agents and for fix and switching directional topology has been investigated, and then by simulation, the results, and the convergence rate has been compared with previous works that used scalar signal. Since for the design of the control inputs of the network, the data of each agent(drone) is obtained from the integration of the local state of that agent with the state of the neighboring agents which is affected by random communication noise, the necessary and sufficient conditions to reach the average consensus for the fixed and switching directional topology has been obtained.