Cooperative distributed MPC via decentralized real-time optimization: Implementation results for robot formations

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