Reference Tracking for Multiagent Systems Using Model Predictive Control

Abstract

In this note, the reference tracking problem for teams of unmanned vehicles subject to formation constraints is solved via a model predictive control (MPC) algorithm built up in a distributed fashion. By exploiting the properties deriving from a novel kinematic description of the swarm agents, the receding horizon control (RHC) approach is properly adapted to deal with tracking and formation constraints. In particular, neighbor interactions are translated into convex conditions, thanks to an in-depth analysis of the geometric properties arising from the combined use of swarm kinematics and state predictions tubes. Experimental results on Elisa-3 robots show the applicability and effectiveness of the proposed control architecture.