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.

翻译：分布式模型预测控制（DMPC）是一种灵活且可扩展的反馈控制方法，适用于多种系统。尽管DMPC的稳定性分析已较为完善，但涉及分布式计算和网络通信的实际应用实现结果仍然有限。本文通过一种协同DMPC方案实现移动机器人的编队控制，并探讨了基于分散式优化算法的实现方法。为此，我们将交替方向乘子法与分散式序列二次规划相结合，以分散式方式求解底层最优控制问题，并具备名义收敛保证。该方法仅需耦合子系统之间进行通信，无需依赖中央协调器。实验结果表明，DMPC在编队控制中具有显著有效性，同时验证了所考虑算法的实时可行性。