Distributed Average Consensus under Quantized Communication via Event-Triggered Mass Summation
It addresses the problem of exact average consensus under communication constraints (quantization and event-triggering) for multi-agent systems, which is a known bottleneck in distributed control.
This paper presents a distributed averaging algorithm for multi-agent systems with quantized communication and event-triggered updates, achieving exact average consensus in finite time on strongly connected digraphs.
We study distributed average consensus problems in multi-agent systems with directed communication links that are subject to quantized information flow. The goal of distributed average consensus is for the nodes, each associated with some initial value, to obtain the average (or some value close to the average) of these initial values. In this paper, we present and analyze a distributed averaging algorithm which operates exclusively with quantized values (specifically, the information stored, processed and exchanged between neighboring agents is subject to deterministic uniform quantization) and relies on event-driven updates (e.g., to reduce energy consumption, communication bandwidth, network congestion, and/or processor usage). We characterize the properties of the proposed distributed averaging protocol on quantized values and show that its execution, on any time-invariant and strongly connected digraph, will allow all agents to reach, in finite time, a common consensus value represented as the ratio of two integer that is equal to the exact average. We conclude with examples that illustrate the operation, performance, and potential advantages of the proposed algorithm.