Unconstrained Distributed Model Predictive Control for Double-Integrator Systems with Switching Interaction Graph

A new unconstrained distributed model predictive control (DMPC) is extended for double-integrator systems with switching interaction graph. The task is to stabilize a static formation in the leader-follower framework. The proposed DMPC is implemented in a receding horizon way, where the predicted own control and neighboring controls are calculated as decision variables of optimization, but only the own optimized current control is exerted. Sufficient conditions are provided, such that the static formation can be stabilized asymptotically by the proposed DMPC in case of switching interaction graph.