  In control system networks, reconfiguration of the controller when agents are
leaving or joining the network is still an open challenge, in particular when
operation constraints that depend on each agent's behavior must be met. Drawing
our motivation from mobile robot swarms, in this paper, we address this problem
by optimizing individual agent performance while guaranteeing persistent
constraint satisfaction in presence of bounded communication range and
time-varying network topology. The approach we propose is a model predictive
control (MPC) formulation, building on multi-trajectory MPC (mt-MPC) concepts.
To enable plug and play operations when the system is in closed-loop without
the need of a request, the proposed MPC scheme predicts two different state
trajectories in the same finite horizon optimal control problem. One trajectory
drives the system to the desired target, assuming that the network topology
will not change in the prediction horizon, while the second one ensures
constraint satisfaction assuming a worst-case scenario in terms of new agents
joining the network in the planning horizon. Recursive feasibility and
stability of the closed-loop system during plug and play operations are shown.
The approach effectiveness is illustrated with a numerical simulation.
