CONSENSUS IN MULTI-AGENT SYSTEMS

Many cooperative behaviors of multi-agent teams emerge from local interactions among the agents, where an agent interacts with a few “adjacent” teammates, but has no information about the remaining agents. For instance, the selforganization of many biological populations – including swarms of insects, flocks of birds, and schools of fish – are based on such local interaction rules: the motion and decisions of an individual agent are determined by the behavior of its nearest neighbors in the population. A special case of multi-agent coordination is consensus, that is, the agreement of agents on some quantity of interest or, more generally, the full or partial synchronization of their state trajectories. Establishing consensus is a “benchmark” problem in multi-agent systems study, which allows to reveal the main principles of multi-agent coordination and, in particular, the role of the system’s interaction graph (or topology). Consensus lies in the heart of many natural phenomena (e.g., synchronous oscillation of neural cells, which maintains a stable heart rhythm) and engineering designs (e.g., attitude synchronization of satellites). In this article, we give a brief review of distributed consensus algorithms, focusing on the basic ideas and relevant mathematical theory, in particular, graph theoretic methods.