Distributed Fault Detection and Accommodation in Dynamic Average Consensus

TL;DR

This paper develops fault detection and accommodation strategies for autonomous agents using dynamic average consensus algorithms, ensuring reliable consensus despite communication faults.

Contribution

It introduces fault detection filters and remediation schemes tailored for two types of consensus algorithms, enhancing robustness in fault-prone networks.

Findings

Effective fault detection filters based on unknown input observers.

Fault accommodation schemes enable consensus despite communication faults.

Applicable to both stable and non-robust consensus algorithms.

Abstract

This paper presents the formulation of fault detection and accommodation schemes for a network of autonomous agents running internal model-based dynamic average consensus algorithms. We focus on two types of consensus algorithms, one that is internally stable but non-robust to initial conditions and one that is robust to initial conditions but not internally stable. For each consensus algorithm, a fault detection filter based on the unknown input observer scheme is developed for precisely estimating the communication faults that occur on the network edges. We then propose a fault remediation scheme so that the agents could reach average consensus even in the presence of communication faults.