Distributed Omniscient Observers for Multi-Agent Systems: Design and Applications

This paper proposes distributed omniscient observers for both heterogeneous and homogeneous linear multi-agent systems, such that each agent can correctly estimate the states of all agents. The observer design is based on local input-output information available to each agent, and knowledge of the global communication graph among agents is not necessarily required. The proposed observers can contribute to distributed Nash equilibrium seeking in multi-player games and the emergence of self-organized social behaviors in artificial swarms. Simulation results demonstrate that artificial swarms can emulate animal social behaviors, including sheepdog herding and honeybee dance-based navigation.

💡 Research Summary

The paper introduces a novel class of distributed observers—called Distributed Omniscient Observers (DOOs)—that enable every agent in a linear multi‑agent system (MAS) to reconstruct the full state vector of the entire network. Unlike traditional distributed observers, which typically rely on absolute measurements or require global knowledge of the communication topology, the proposed DOOs operate using only locally available input‑output data and, optionally, relative measurements between neighboring agents. The design builds on the framework of a recent distributed observer (reference