Distributed model predictive control (DMPC) is a flexible and scalable feedback control method applicable to a wide range of systems. While 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. 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.

中文翻译：

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通过分散实时优化的协作分布式 MPC：机器人编队的实施结果

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，适用于广泛的系统。虽然 DMPC 的稳定性分析已广为人知，但对于涉及分布式计算和网络通信的实际应用，只有有限的实施结果。本文通过协作 DMPC 方案研究移动机器人的编队控制。我们通过分散优化算法讨论实现。为此，我们将乘法器的交替方向法与分散的顺序二次规划相结合，以分散的方式解决潜在的最优控制问题。我们的方法只需要耦合的子系统进行通信，而不依赖于中央协调器。