Networked multi-agent systems consist of interacting agents that locally exchange information, energy, or matter. Since these systems do not in general have a centralized architecture to monitor the activity of each agent, resilient distributed control system design for networked multi-agent systems is essential in providing high system performance, reliability, and operation in the presence of system uncertainties. An important class of such system uncertainties that can significantly deteriorate the achievable closed-loop system performance is sensor uncertainties, which can arise due to low sensor quality, sensor failure, sensor bias, or detrimental environmental conditions. This paper presents a novel distributed adaptive control architecture for networked multi-agent systems with undirected communication graph topologies to mitigate the effect of sensor uncertainties. Specifically, we consider agents having identical high-order, linear dynamics with agent interactions corrupted by unknown exogenous disturbances. We show that the proposed adaptive control architecture guarantees asymptotic stability of the closed-loop dynamical system when the exogenous disturbances are time-invariant and uniform ultimate boundedness when the exogenous disturbances are time-varying. Two numerical examples are provided to illustrate the efficacy of the proposed distributed adaptive control architecture.
Distributed Adaptive Control: An Ideal Cognitive Architecture Candidate for Managing a Robotic Recycling Plant