# 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.

## Key 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.