  Distributed model predictive control (DMPC) is a flexible and scalable
feedback control method applicable to a wide range of systems. While the
stability analysis of DMPC is quite well understood, there exist only limited
implementation results for realistic applications involving distributed
computation and networked communication. This article approaches formation
control of mobile robots via a cooperative DMPC scheme. We discuss the
implementation via decentralized optimization algorithms. To this end, we
combine the alternating direction method of multipliers with decentralized
sequential quadratic programming to solve the underlying optimal control
problem in a decentralized fashion with nominal convergence guarantees. Our
approach only requires coupled subsystems to communicate and does not rely on a
central coordinator. Our experimental results showcase the efficacy of DMPC for
formation control and they demonstrate the real-time feasibility of the
considered algorithms.
