  Active-passive multiagent systems consist of agents subject to inputs (active
agents) and agents with no inputs (passive agents), where active and passive
agent roles are considered to be interchangeable in order to capture a wide
array of applications. A challenge in the control of active-passive multiagent
systems is the presence of information exchange uncertainties that can yield to
undesirable closed-loop system performance. Motivated by this standpoint, this
paper proposes an adaptive control algorithm for this class of multiagent
systems to suppress the negative effects of information exchange uncertainties.
Specifically, by estimating these uncertainties, the proposed adaptive control
architecture has the ability to recover the active-passive multiagent system
performance in a distributed manner. As a result, the agents converge to a
user-adjustable neighborhood of the average of the applied inputs to the active
agents. The efficacy of the proposed control architecture is also validated
from a human-robot collaboration perspective, where a human is visiting several
task locations, and the multiagent system identifies these locations and moves
toward them as a coverage control problem.
