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Distributed Fault Detection and Accommodation in Dynamic Average Consensus
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