Distributed control algorithms are known to reduce overall computation time compared to centralized control algorithms. However, they can result in inconsistent solutions leading to the violation of safety-critical constraints. Inconsistent solutions can arise when two or more agents compute concurrently while making predictions on each others control actions. To address this issue, we propose an iterative algorithm called Synchronization-Based Cooperative Distributed Model Predictive Control, which we presented in [1]. The algorithm consists of two steps: 1. computing the optimal control inputs for each agent and 2. synchronizing the predicted states across all agents. We demonstrate the efficacy of our algorithm in the control of multiple small-scale vehicles in our Cyber-Physical Mobility Lab.

翻译：相较于集中式控制算法，分布式控制算法能够显著降低整体计算时间。然而，这类算法可能导致解的不一致性，从而违反安全关键约束。当两个或多个智能体在预测彼此控制动作的同时并行计算时，便可能产生不一致的解。为解决此问题，我们提出了一种称为"基于同步的协作式分布式模型预测控制"的迭代算法，该算法已在文献[1]中阐述。该算法包含两个步骤：1. 计算各智能体的最优控制输入；2. 在所有智能体间同步预测状态。我们在信息物理移动实验室中通过多台小型车辆的控制实验，验证了该算法的有效性。