  We address the multi-agent persistent monitoring problem defined on a set of
nodes (targets) interconnected over a network topology. A measure of mean
overall node state uncertainty evaluated over a finite period is to be
minimized by controlling the motion of a cooperating team of agents. To address
this problem, we propose an event-driven receding horizon control approach that
is computationally efficient, distributed and on-line. The proposed controller
differs from the existing on-line gradient-based parametric controllers and
off-line greedy cycle search methods that often lead to either low-performing
local optima or computationally intensive centralized solutions. A critical
novel element in this controller is that it automatically optimizes its
planning horizon length, thus making it parameter-free. We show that explicit
globally optimal solutions can be obtained for every distributed optimization
problem encountered at each event where the receding horizon controller is
invoked. Numerical results are provided showing improvements compared to state
of the art distributed on-line parametric control solutions.
