Minimum-Energy Distributed Consensus of Uncertain Agents

TL;DR

This paper introduces a minimum-energy consensus algorithm for multi-agent systems with uncertain, noisy measurements, ensuring exponential convergence and robustness against disturbances through a novel estimation approach.

Contribution

It proposes a new distributed consensus method that effectively handles measurement uncertainties and disturbances, improving robustness and convergence in multi-agent systems.

Findings

Converges exponentially without disturbances

Maintains consensus under bounded disturbances

Effective with large measurement errors

Abstract

This paper presents a consensus algorithm for a multi-agent system where each agent has access to its imperfect own state and neighboring state measurements. The measurements are subject to deterministic disturbances and the proposed algorithm provides a minimum-energy estimate of the measured states which is instrumental in achieving consensus by the nodes. It is shown that the proposed consensus algorithm converges exponentially in the absence of disturbances, and its performance under bounded continuous disturbances is investigated as well. The convergence performance of the proposed method is further studied using simulations where we show that consensus is achieved despite using large measurement errors.