发表机构
ETH Zurich（苏黎世联邦理工学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对分布式模型预测控制中通信连通性难以保证的问题，提出基于连通性契约的DMPC框架，通过局部契约确保团队连通，并证明闭环系统满足约束，仿真与硬件实验验证了有效性。
AI 中文摘要
移动机器人团队依靠与邻居的持续通信进行协调，然而大多数分布式模型预测控制（DMPC）方案假设通信网络保持连通，而非主动强制实现连通性。添加这样的保证是困难的，因为通常的连通性数学条件是非凸的，并且将每个智能体与所有其他智能体相连，这与可扩展的分布式实时控制器不相兼容。我们提出了一种DMPC框架，其中每个智能体被分配一个连通性契约：一个局部区域，规定其预测位置在预测时域内可能位于何处。这些契约的设计使得，只要每个智能体都停留在其自身的契约内，团队就能保证保持连通。在给定维护的契约图的情况下，一个智能体通过与其直接邻居的单次交换来构建其契约，之后每个智能体独立地求解其自身的优化问题。我们证明了所得到的闭环系统能够保持连通性、避免碰撞，并满足局部状态和输入约束。在微型自主类车机器人上的仿真和硬件实验验证了该方法。
英文摘要
Teams of mobile robots rely on continuous communication with their neighbors for coordination, yet most distributed model predictive control (DMPC) schemes assume the communication network stays connected rather than actively enforcing it. Adding such a guarantee is hard since the usual mathematical condition for connectivity is nonconvex and links every agent to every other, which is incompatible with a scalable distributed real-time controller. We propose a DMPC framework in which each agent is assigned a connectivity contract: a local region prescribing where its predicted positions may lie over the prediction horizon. The contracts are designed so that, as long as every agent stays within its own contract, the team is guaranteed to remain connected. Given the maintained contract graph, an agent builds its contract from a single exchange with its immediate neighbors, after which every agent solves its own optimization problem independently. We prove that the resulting closed-loop system maintains connectivity, avoids collisions, and respects local state and input constraints. Simulation and hardware experiments on miniature autonomous car-like robots demonstrate the approach.
