A Supervisory Algorithm Against Intermittent and Temporary Faults in Consensus-Based Networks

Abstract

In this paper, the consensus problem is addressed for multi-agent networks in which some nodes are expected to possibly misbehave as a consequence of temporary faults or errors. The solution proposed is based on the counteraction of a prescribed subset of nodes through the injection of additional corrective signals. Such corrections are the output of a fault compensation algorithm based on a simple elaboration of local data, whose structure is based on some input-output theoretical results achieved in this paper. A thorough discussion is reported on how to retrieve the algorithm coefficients using local data, so that the algorithm can be fully performed without any network topology knowledge in advance. Simulation results show the behavior of the adopted strategy in several different faulty conditions, and robustness of the compensation algorithm with respect to errors in the computation of the coefficients is also investigated. The results show that the proposed algorithm performs well in several scenarios of networked systems, and it can be suited to easily restore the consensus value to the original one, in spite of the drift initially caused by the fault.

Keywords

• Collaborative sensor networks
• consensus networks
• fault compensation algorithms
• robotic networks